library(ggplot2)
library(survminer)
## Loading required package: ggpubr
library(survival)
library(dplyr)
##
## Attaching package: 'dplyr'
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## filter, lag
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library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ tibble 3.1.0 ✓ purrr 0.3.4
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
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## x dplyr::filter() masks stats::filter()
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#library(data.table)
library(forestmodel)
#library(Hmisc)
library(sjPlot)
## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car
library(stargazer)
##
## Please cite as:
## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
library(sjmisc)
##
## Attaching package: 'sjmisc'
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## is_empty
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## add_case
library(arsenal)
##
## Attaching package: 'arsenal'
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## %nin%
library(gtsummary)
library(expss)
##
## Use 'expss_output_viewer()' to display tables in the RStudio Viewer.
## To return to the console output, use 'expss_output_default()'.
##
## Attaching package: 'expss'
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library(lubridate)
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library(ggsignif)
library(ggsci)
library(Greg) #to use timesplitter
## Loading required package: forestplot
## Loading required package: grid
## Loading required package: magrittr
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## Attaching package: 'magrittr'
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## [.labelled expss
## print.labelled expss
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library(magrittr)
options(scipen=999)
#Random effects
#https://stats.idre.ucla.edu/r/dae/mixed-effects-cox-regression/
library("coxme")
## Loading required package: bdsmatrix
##
## Attaching package: 'bdsmatrix'
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## backsolve
Read in data
bound2 <- read_csv("/Users/Ivanics/Desktop/GitHub/International-LDLT/Datafile/merged.csv", guess_max = 300000)
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## DTYPE = col_character(),
## DONCOD = col_character(),
## DCMV = col_character(),
## DSEX = col_character(),
## BLD_GP_MATCH = col_character(),
## GRAFT_TYPE = col_character(),
## RSEX = col_character(),
## RETHNIC = col_character(),
## TRANSPLANT_UNIT = col_character(),
## RREN_SUP = col_character(),
## RVENT = col_character(),
## RAB_SURGERY = col_character(),
## RLIFE = col_character(),
## RASCITES = col_character(),
## RENCEPH = col_character(),
## RBG = col_character(),
## RANTI_HCV = col_character(),
## COUNTRY = col_character(),
## HCC_combined = col_character(),
## UKT_PLDGRP = col_character()
## # ... with 5 more columns
## )
## ℹ Use `spec()` for the full column specifications.
bound2 <- bound2 %>% filter(COUNTRY == "US" | COUNTRY == "UK" | COUNTRY == "CAN")
bound2 <- bound2 %>% filter(TX_YR <= 2018)
bound2 <- bound2 %>%
mutate(PSURV_years = PSURV/365.25) %>%
mutate(GSURV_years = GSURV/365.25)
bound2$COUNTRY <- factor(bound2$COUNTRY, ordered = FALSE)
bound2$COUNTRY <- relevel(bound2$COUNTRY, ref="US")
bound2$RVENT <- factor(bound2$RVENT, ordered = FALSE)
bound2$RVENT <- relevel(bound2$RVENT, ref = "Not ventilated")
bound2$RETHNIC <- factor(bound2$RETHNIC, ordered = FALSE)
bound2$RETHNIC <- relevel(bound2$RETHNIC, ref = "White")
bound2$DCMV <- factor(bound2$DCMV, ordered = FALSE)
bound2$DCMV <- relevel(bound2$DCMV, ref = "Negative")
bound2$HCC <- factor(bound2$HCC)
bound2$HCV <- factor(bound2$HCV)
bound2$ALD <- factor(bound2$ALD)
bound2$NASH <- factor(bound2$NASH)
bound2$PSC <- factor(bound2$PSC)
bound2$PBC <- factor(bound2$PBC)
bound2$MET <- factor(bound2$MET)
bound2$AID <- factor(bound2$AID)
bound2$TRANSPLANT_UNIT <- factor(bound2$TRANSPLANT_UNIT)
bound2 <- bound2 %>% mutate(LDLT = case_when(
DTYPE == "LDLT" & GRAFT_TYPE == "Segmental" ~ 1,
TRUE ~ 0
)) %>% mutate(LDLT = factor(LDLT))
bound2 <- bound2 %>% mutate(MELD_calculated = 3.78*log(RBILIRUBIN) + 11.2*log(RINR) + 9.57*log(RCREAT) + 6.43)
Recoding graft survival
#bound2 <- bound2 %>% mutate(
# GCENSnew = case_when(
# PCENS == 1 & PSURV_years <= GSURV_years ~ 1,
# TRUE ~ 0
# )
#)
#bound2 <- bound2 %>% mutate(
# GSURV_yearsnew = case_when(
# PCENS == 1 & PSURV_years <= GSURV_years ~ PSURV_years,
# TRUE ~ GSURV_years
# )
#)
Recoding patient survival
#1year PSURV
bound2 <- bound2 %>% mutate(PSURV_1year =
case_when(
PSURV_years >= 1.0000001 ~ 1,
PSURV_years <= 1 ~ PSURV_years
))
#1-year PCENS
bound2 <- bound2 %>% mutate(PCENS_1year =
case_when(
PSURV_years <= 1 ~ PCENS,
PSURV_years > 1 ~ 0
))
#3year PSURV
bound2 <- bound2 %>% mutate(PSURV_3year =
case_when(
PSURV_years >= 3.000001 ~ 3,
PSURV_years <= 3 ~ PSURV_years
))
#3-year PCENS
bound2 <- bound2 %>% mutate(PCENS_3year =
case_when(
PSURV_years <= 3 ~ PCENS,
PSURV_years > 3 ~ 0
))
#5year PSURV
bound2 <- bound2 %>% mutate(PSURV_5year =
case_when(
PSURV_years >= 5.000001 ~ 5,
PSURV_years <= 5 ~ PSURV_years
))
#5-year PCENS
bound2 <- bound2 %>% mutate(PCENS_5year =
case_when(
PSURV_years <= 5 ~ PCENS,
PSURV_years > 5 ~ 0
))
#10year PSURV
bound2 <- bound2 %>% mutate(PSURV_10year =
case_when(
PSURV_years >= 10.000001 ~ 10,
PSURV_years <= 10 ~ PSURV_years
))
#5-year PCENS
bound2 <- bound2 %>% mutate(PCENS_10year =
case_when(
PSURV_years <= 10 ~ PCENS,
PSURV_years > 10 ~ 0
))
Recoding graft survival
#1year GSURV
bound2 <- bound2 %>% mutate(GSURV_1year =
case_when(
GSURV_years >= 1.0000001 ~ 1,
GSURV_years <= 1 ~ GSURV_years
))
#1-year GCENS
bound2 <- bound2 %>% mutate(GCENS_1year =
case_when(
GSURV_years <= 1 ~ GCENS,
GSURV_years > 1 ~ 0
))
#3year GSURV
bound2 <- bound2 %>% mutate(GSURV_3year =
case_when(
GSURV_years >= 3.000001 ~ 3,
GSURV_years <= 3 ~ GSURV_years
))
#3-year GCENS
bound2 <- bound2 %>% mutate(GCENS_3year =
case_when(
GSURV_years <= 3 ~ GCENS,
GSURV_years > 3 ~ 0
))
#5year GSURV
bound2 <- bound2 %>% mutate(GSURV_5year =
case_when(
GSURV_years >= 5.000001 ~ 5,
GSURV_years <= 5 ~ GSURV_years
))
#5-year GCENS
bound2 <- bound2 %>% mutate(GCENS_5year =
case_when(
GSURV_years <= 5 ~ GCENS,
GSURV_years > 5 ~ 0
))
#10year GSURV
bound2 <- bound2 %>% mutate(GSURV_10year =
case_when(
GSURV_years >= 10.000001 ~ 10,
GSURV_years <= 10 ~ GSURV_years
))
#5-year GCENS
bound2 <- bound2 %>% mutate(GCENS_10year =
case_when(
GSURV_years <= 10 ~ GCENS,
GSURV_years > 10 ~ 0
))
bound3 <- bound2
bound3 <- bound3 %>% filter(!(DTYPE == "LDLT" & GRAFT_TYPE == "Whole"))
bound2 <- bound2 %>% filter(DTYPE == "LDLT" & GRAFT_TYPE == "Segmental")
#creatining a variable for center count (number of appearances of a center in LT pts)
bound2 <- bound2 %>%
group_by(TRANSPLANT_UNIT, TX_YR) %>%
mutate(count = n()) %>%
ungroup
summary(bound2$count)
Min. 1st Qu. Median Mean 3rd Qu. Max. 1.00 7.00 13.00 17.12 22.00 58.00
#creating a variable for center experience according to percentile of count
bound2 <- bound2 %>%
mutate(Centerpercentile =
case_when(
count <7 ~ 1,
count >=7 & count < 22 ~2,
count >=22 ~3,
)) %>%
mutate(Centerpercentile = factor(Centerpercentile, labels = c("<25%ile", "25-75%ile", ">75%ile")))
Crosstabulating
#ftable(xtabs(cbind(PCENS,GCENS)~ COUNTRY, data=bound2))
#ftable(xtabs(cbind(PCENS,GCENSnew)~ COUNTRY, data=bound2))
bound3 <- bound3 %>% mutate(DTYPE_DDLTandLDLT = case_when(
DTYPE == "DBD" ~ 0,
DTYPE == "DCD" ~ 0,
DTYPE == "LDLT" ~ 1
)) %>% mutate(DTYPE_DDLTandLDLT = factor(DTYPE_DDLTandLDLT, labels = c("DDLT", "LDLT")))
bound3_2008 <- bound3 %>% filter(TX_YR == 2008)
tab1 <- tableby(COUNTRY ~
DTYPE_DDLTandLDLT,
data=bound3_2008, test=TRUE, total=TRUE,
numeric.stats=c("medianq1q3"), numeric.test="kwt", cat.test="chisq")
summary(tab1, title='Graft types 2008', pfootnote=TRUE, digits = 2)
| US (N=4920) | CAN (N=284) | UK (N=473) | Total (N=5677) | p value | |
|---|---|---|---|---|---|
| DTYPE_DDLTandLDLT | < 0.0011 | ||||
| DDLT | 4758 (96.7%) | 227 (79.9%) | 465 (98.3%) | 5450 (96.0%) | |
| LDLT | 162 (3.3%) | 57 (20.1%) | 8 (1.7%) | 227 (4.0%) |
bound3_2018 <- bound3 %>% filter(TX_YR == 2018)
tab1 <- tableby(COUNTRY ~
DTYPE_DDLTandLDLT,
data=bound3_2018, test=TRUE, total=TRUE,
numeric.stats=c("medianq1q3"), numeric.test="kwt", cat.test="chisq")
summary(tab1, title='Graft types 2018', pfootnote=TRUE, digits = 2)
| US (N=6655) | CAN (N=348) | UK (N=771) | Total (N=7774) | p value | |
|---|---|---|---|---|---|
| DTYPE_DDLTandLDLT | < 0.0011 | ||||
| DDLT | 6327 (95.1%) | 301 (86.5%) | 768 (99.6%) | 7396 (95.1%) | |
| LDLT | 328 (4.9%) | 47 (13.5%) | 3 (0.4%) | 378 (4.9%) |
tab1 <- tableby(COUNTRY ~
CIT+
DAGE+
DBMI+
DTYPE+
DSEX+
GRAFT_TYPE+
DTYPE+
DCMV,
data=bound2, test=TRUE, total=TRUE,
numeric.stats=c("medianq1q3"), numeric.test="kwt", cat.test="chisq")
summary(tab1, title='Donor characteristics', pfootnote=TRUE, digits = 2)
| US (N=2433) | CAN (N=556) | UK (N=97) | Total (N=3086) | p value | |
|---|---|---|---|---|---|
| CIT | 0.0031 | ||||
| Median (Q1, Q3) | 84.90 (55.80, 120.00) | 87.00 (57.00, 127.00) | 110.00 (72.00, 162.00) | 86.00 (57.00, 123.00) | |
| DAGE | 0.0101 | ||||
| Median (Q1, Q3) | 36.00 (28.00, 45.00) | 35.00 (27.00, 46.00) | 32.50 (25.00, 40.00) | 36.00 (28.00, 45.00) | |
| DBMI | |||||
| Median (Q1, Q3) | 25.94 (23.46, 28.58) | 25.34 (23.09, 28.33) | NA | 25.90 (23.45, 28.57) | |
| DTYPE | < 0.0012 | ||||
| LDLT | 2433 (100.0%) | 556 (100.0%) | 97 (100.0%) | 3086 (100.0%) | |
| DSEX | < 0.0013 | ||||
| Female | 1280 (52.6%) | 242 (43.5%) | 35 (36.1%) | 1557 (50.5%) | |
| Male | 1153 (47.4%) | 314 (56.5%) | 62 (63.9%) | 1529 (49.5%) | |
| GRAFT_TYPE | < 0.0012 | ||||
| Segmental | 2433 (100.0%) | 556 (100.0%) | 97 (100.0%) | 3086 (100.0%) | |
| DCMV | |||||
| N-Miss | 2433 | 433 | 97 | 2963 | |
| Negative | 0 | 71 (57.7%) | 0 | 71 (57.7%) | |
| Positive | 0 | 52 (42.3%) | 0 | 52 (42.3%) |
bound3 <- bound3 %>% mutate(UHN = factor(UHN, labels = c("Not UHN", "UHN")))
tab1 <- tableby(UHN ~
DTYPE,
data=bound3, subset = COUNTRY == "CAN", test=TRUE, total=TRUE,
numeric.stats=c("medianq1q3"), numeric.test="kwt", cat.test="chisq")
summary(tab1, title='', pfootnote=TRUE, digits = 2)
| Not UHN (N=2040) | UHN (N=1586) | Total (N=3626) | p value | |
|---|---|---|---|---|
| DTYPE | < 0.0011 | |||
| DBD | 1715 (84.1%) | 1055 (66.5%) | 2770 (76.4%) | |
| DCD | 190 (9.3%) | 110 (6.9%) | 300 (8.3%) | |
| LDLT | 135 (6.6%) | 421 (26.5%) | 556 (15.3%) |
tab1 <- tableby(COUNTRY ~
RSEX+
RETHNIC+
MELD+
MELD_calculated+
RREN_SUP+
RAGE+
RLIFE+
RVENT+
WAITLIST_TIME+
TX_YR+
BMI+
RAB_SURGERY+
RCREAT+
RBILIRUBIN+
RALBUMIN+
RINR+
RANTI_HCV+
RBG+
RASCITES+
RENCEPH+
BLD_GP_MATCH+
HCC +
HCV +
ALD +
NASH +
PSC +
PBC +
AID+
PSURV_years +
factor(UHN),
data=bound2, test=TRUE, total=TRUE,
numeric.stats=c("medianq1q3"), numeric.test="kwt", cat.test="chisq")
summary(tab1, title='Recipient characteristics', pfootnote=TRUE, digits = 2)
| US (N=2433) | CAN (N=556) | UK (N=97) | Total (N=3086) | p value | |
|---|---|---|---|---|---|
| RSEX | 0.1771 | ||||
| Female | 1082 (44.5%) | 242 (43.5%) | 52 (53.6%) | 1376 (44.6%) | |
| Male | 1351 (55.5%) | 314 (56.5%) | 45 (46.4%) | 1710 (55.4%) | |
| RETHNIC | < 0.0011 | ||||
| White | 1989 (81.8%) | 123 (22.1%) | 51 (52.6%) | 2163 (70.1%) | |
| Black | 80 (3.3%) | 1 (0.2%) | 1 (1.0%) | 82 (2.7%) | |
| Other | 364 (15.0%) | 432 (77.7%) | 45 (46.4%) | 841 (27.3%) | |
| MELD | < 0.0012 | ||||
| Median (Q1, Q3) | 15.00 (11.00, 19.00) | 17.00 (13.00, 21.00) | 15.00 (11.00, 18.00) | 15.00 (11.00, 19.00) | |
| MELD_calculated | < 0.0012 | ||||
| Median (Q1, Q3) | 12.65 (8.57, 17.03) | 14.81 (9.99, 18.73) | 11.07 (7.77, 15.64) | 12.72 (8.65, 17.17) | |
| RREN_SUP | 0.0131 | ||||
| N-Miss | 5 | 0 | 6 | 11 | |
| No pre-tx support | 2412 (99.3%) | 549 (98.7%) | 88 (96.7%) | 3049 (99.2%) | |
| Pre-tx support | 16 (0.7%) | 7 (1.3%) | 3 (3.3%) | 26 (0.8%) | |
| RAGE | < 0.0012 | ||||
| Median (Q1, Q3) | 56.00 (46.00, 62.00) | 54.00 (45.00, 61.00) | 53.00 (35.00, 61.00) | 55.00 (46.00, 62.00) | |
| RLIFE | |||||
| N-Miss | 31 | 556 | 1 | 588 | |
| High | 742 (30.9%) | 0 | 12 (12.5%) | 754 (30.2%) | |
| Intermediate | 1258 (52.4%) | 0 | 36 (37.5%) | 1294 (51.8%) | |
| Low | 402 (16.7%) | 0 | 48 (50.0%) | 450 (18.0%) | |
| RVENT | |||||
| N-Miss | 0 | 556 | 1 | 557 | |
| Not ventilated | 2421 (99.5%) | 0 | 96 (100.0%) | 2517 (99.5%) | |
| Ventilated | 12 (0.5%) | 0 | 0 (0.0%) | 12 (0.5%) | |
| WAITLIST_TIME | < 0.0012 | ||||
| Median (Q1, Q3) | 151.00 (79.00, 308.00) | 122.00 (63.50, 230.00) | 91.50 (7.00, 201.75) | 145.00 (75.00, 290.00) | |
| TX_YR | < 0.0012 | ||||
| Median (Q1, Q3) | 2014.00 (2011.00, 2017.00) | 2013.00 (2010.00, 2015.00) | 2013.00 (2011.00, 2015.00) | 2014.00 (2011.00, 2016.00) | |
| BMI | < 0.0012 | ||||
| Median (Q1, Q3) | 26.25 (23.25, 29.86) | 25.67 (22.67, 29.05) | 24.24 (21.91, 28.20) | 26.05 (23.11, 29.68) | |
| RAB_SURGERY | |||||
| N-Miss | 37 | 556 | 1 | 594 | |
| No | 1195 (49.9%) | 0 | 78 (81.2%) | 1273 (51.1%) | |
| Yes | 1201 (50.1%) | 0 | 18 (18.8%) | 1219 (48.9%) | |
| RCREAT | 0.0582 | ||||
| Median (Q1, Q3) | 0.85 (0.70, 1.10) | 0.82 (0.70, 1.05) | 0.77 (0.57, 0.97) | 0.84 (0.69, 1.10) | |
| RBILIRUBIN | < 0.0012 | ||||
| Median (Q1, Q3) | 2.70 (1.47, 4.80) | 3.71 (1.99, 7.27) | 2.49 (1.35, 4.50) | 2.80 (1.50, 5.00) | |
| RALBUMIN | |||||
| Median (Q1, Q3) | 3.10 (2.70, 3.60) | NA | 3.20 (2.68, 3.82) | 3.10 (2.70, 3.60) | |
| RINR | 0.0392 | ||||
| Median (Q1, Q3) | 1.40 (1.20, 1.60) | 1.44 (1.20, 1.79) | 1.40 (1.20, 1.90) | 1.40 (1.20, 1.66) | |
| RANTI_HCV | 0.7601 | ||||
| N-Miss | 76 | 111 | 0 | 187 | |
| Negative | 1794 (76.1%) | 339 (76.2%) | 77 (79.4%) | 2210 (76.2%) | |
| Positive | 563 (23.9%) | 106 (23.8%) | 20 (20.6%) | 689 (23.8%) | |
| RBG | < 0.0011 | ||||
| N-Miss | 0 | 1 | 0 | 1 | |
| 0 | 1112 (45.7%) | 237 (42.7%) | 47 (48.5%) | 1396 (45.3%) | |
| A | 1041 (42.8%) | 206 (37.1%) | 21 (21.6%) | 1268 (41.1%) | |
| AB | 34 (1.4%) | 25 (4.5%) | 2 (2.1%) | 61 (2.0%) | |
| B | 246 (10.1%) | 87 (15.7%) | 27 (27.8%) | 360 (11.7%) | |
| RASCITES | |||||
| N-Miss | 0 | 556 | 0 | 556 | |
| Ascites | 1572 (64.6%) | 0 | 44 (45.4%) | 1616 (63.9%) | |
| No ascites | 861 (35.4%) | 0 | 53 (54.6%) | 914 (36.1%) | |
| RENCEPH | |||||
| N-Miss | 0 | 556 | 3 | 559 | |
| Encephalopathic | 1244 (51.1%) | 0 | 22 (23.4%) | 1266 (50.1%) | |
| Not encephalopathic | 1189 (48.9%) | 0 | 72 (76.6%) | 1261 (49.9%) | |
| BLD_GP_MATCH | |||||
| N-Miss | 0 | 556 | 2 | 558 | |
| Compatible | 539 (22.2%) | 0 | 21 (22.1%) | 560 (22.2%) | |
| Identical | 1880 (77.3%) | 0 | 73 (76.8%) | 1953 (77.3%) | |
| Incompatible | 14 (0.6%) | 0 | 1 (1.1%) | 15 (0.6%) | |
| HCC | 0.5751 | ||||
| 0 | 2023 (83.1%) | 454 (81.7%) | 78 (80.4%) | 2555 (82.8%) | |
| 1 | 410 (16.9%) | 102 (18.3%) | 19 (19.6%) | 531 (17.2%) | |
| HCV | < 0.0011 | ||||
| 0 | 2383 (97.9%) | 488 (87.8%) | 77 (79.4%) | 2948 (95.5%) | |
| 1 | 50 (2.1%) | 68 (12.2%) | 20 (20.6%) | 138 (4.5%) | |
| ALD | 0.3151 | ||||
| 0 | 2173 (89.3%) | 485 (87.2%) | 88 (90.7%) | 2746 (89.0%) | |
| 1 | 260 (10.7%) | 71 (12.8%) | 9 (9.3%) | 340 (11.0%) | |
| NASH | < 0.0011 | ||||
| 0 | 2029 (83.4%) | 517 (93.0%) | 86 (88.7%) | 2632 (85.3%) | |
| 1 | 404 (16.6%) | 39 (7.0%) | 11 (11.3%) | 454 (14.7%) | |
| PSC | 0.2931 | ||||
| 0 | 2037 (83.7%) | 452 (81.3%) | 78 (80.4%) | 2567 (83.2%) | |
| 1 | 396 (16.3%) | 104 (18.7%) | 19 (19.6%) | 519 (16.8%) | |
| PBC | 0.2181 | ||||
| 0 | 2239 (92.0%) | 500 (89.9%) | 87 (89.7%) | 2826 (91.6%) | |
| 1 | 194 (8.0%) | 56 (10.1%) | 10 (10.3%) | 260 (8.4%) | |
| AID | 0.0761 | ||||
| 0 | 2199 (90.4%) | 516 (92.8%) | 84 (86.6%) | 2799 (90.7%) | |
| 1 | 234 (9.6%) | 40 (7.2%) | 13 (13.4%) | 287 (9.3%) | |
| PSURV_years | < 0.0012 | ||||
| Median (Q1, Q3) | 3.40 (1.21, 6.05) | 4.89 (2.40, 7.60) | 3.18 (1.16, 5.50) | 3.81 (1.52, 6.63) | |
| factor(UHN) | |||||
| N-Miss | 2433 | 0 | 97 | 2530 | |
| 0 | 0 | 135 (24.3%) | 0 | 135 (24.3%) | |
| 1 | 0 | 421 (75.7%) | 0 | 421 (75.7%) |
tab1 <- tableby(COUNTRY ~
factor(TRANSPLANT_UNIT),
data=bound2, test=TRUE, total=TRUE,
numeric.stats=c("medianq1q3"), numeric.test="kwt", cat.test="chisq")
summary(tab1, title='Recipient characteristics', pfootnote=TRUE, digits = 2)
| US (N=2433) | CAN (N=556) | UK (N=97) | Total (N=3086) | p value | |
|---|---|---|---|---|---|
| factor(TRANSPLANT_UNIT) | < 0.0011 | ||||
| 0 | 0 (0.0%) | 135 (24.3%) | 0 (0.0%) | 135 (4.4%) | |
| 00124 | 141 (5.8%) | 0 (0.0%) | 0 (0.0%) | 141 (4.6%) | |
| 00248 | 1 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1 (0.0%) | |
| 00279 | 184 (7.6%) | 0 (0.0%) | 0 (0.0%) | 184 (6.0%) | |
| 00403 | 1 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1 (0.0%) | |
| 00651 | 27 (1.1%) | 0 (0.0%) | 0 (0.0%) | 27 (0.9%) | |
| 01395 | 3 (0.1%) | 0 (0.0%) | 0 (0.0%) | 3 (0.1%) | |
| 01860 | 44 (1.8%) | 0 (0.0%) | 0 (0.0%) | 44 (1.4%) | |
| 02573 | 88 (3.6%) | 0 (0.0%) | 0 (0.0%) | 88 (2.9%) | |
| 02666 | 44 (1.8%) | 0 (0.0%) | 0 (0.0%) | 44 (1.4%) | |
| 03503 | 7 (0.3%) | 0 (0.0%) | 0 (0.0%) | 7 (0.2%) | |
| 03906 | 1 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1 (0.0%) | |
| 04464 | 5 (0.2%) | 0 (0.0%) | 0 (0.0%) | 5 (0.2%) | |
| 05332 | 12 (0.5%) | 0 (0.0%) | 0 (0.0%) | 12 (0.4%) | |
| 05549 | 13 (0.5%) | 0 (0.0%) | 0 (0.0%) | 13 (0.4%) | |
| 05704 | 120 (4.9%) | 0 (0.0%) | 0 (0.0%) | 120 (3.9%) | |
| 06107 | 9 (0.4%) | 0 (0.0%) | 0 (0.0%) | 9 (0.3%) | |
| 06200 | 139 (5.7%) | 0 (0.0%) | 0 (0.0%) | 139 (4.5%) | |
| 06603 | 19 (0.8%) | 0 (0.0%) | 0 (0.0%) | 19 (0.6%) | |
| 07223 | 66 (2.7%) | 0 (0.0%) | 0 (0.0%) | 66 (2.1%) | |
| 07347 | 74 (3.0%) | 0 (0.0%) | 0 (0.0%) | 74 (2.4%) | |
| 07471 | 92 (3.8%) | 0 (0.0%) | 0 (0.0%) | 92 (3.0%) | |
| 07905 | 17 (0.7%) | 0 (0.0%) | 0 (0.0%) | 17 (0.6%) | |
| 08277 | 1 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1 (0.0%) | |
| 08587 | 1 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1 (0.0%) | |
| 09114 | 9 (0.4%) | 0 (0.0%) | 0 (0.0%) | 9 (0.3%) | |
| 09362 | 47 (1.9%) | 0 (0.0%) | 0 (0.0%) | 47 (1.5%) | |
| 1 | 0 (0.0%) | 421 (75.7%) | 0 (0.0%) | 421 (13.6%) | |
| 10044 | 10 (0.4%) | 0 (0.0%) | 0 (0.0%) | 10 (0.3%) | |
| 11067 | 83 (3.4%) | 0 (0.0%) | 0 (0.0%) | 83 (2.7%) | |
| 11129 | 29 (1.2%) | 0 (0.0%) | 0 (0.0%) | 29 (0.9%) | |
| 11191 | 93 (3.8%) | 0 (0.0%) | 0 (0.0%) | 93 (3.0%) | |
| 11470 | 75 (3.1%) | 0 (0.0%) | 0 (0.0%) | 75 (2.4%) | |
| 11532 | 1 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1 (0.0%) | |
| 11749 | 8 (0.3%) | 0 (0.0%) | 0 (0.0%) | 8 (0.3%) | |
| 12834 | 3 (0.1%) | 0 (0.0%) | 0 (0.0%) | 3 (0.1%) | |
| 13237 | 1 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1 (0.0%) | |
| 13485 | 1 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1 (0.0%) | |
| 13919 | 92 (3.8%) | 0 (0.0%) | 0 (0.0%) | 92 (3.0%) | |
| 14477 | 39 (1.6%) | 0 (0.0%) | 0 (0.0%) | 39 (1.3%) | |
| 15283 | 7 (0.3%) | 0 (0.0%) | 0 (0.0%) | 7 (0.2%) | |
| 15438 | 6 (0.2%) | 0 (0.0%) | 0 (0.0%) | 6 (0.2%) | |
| 15593 | 4 (0.2%) | 0 (0.0%) | 0 (0.0%) | 4 (0.1%) | |
| 16523 | 1 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1 (0.0%) | |
| 16616 | 4 (0.2%) | 0 (0.0%) | 0 (0.0%) | 4 (0.1%) | |
| 16864 | 8 (0.3%) | 0 (0.0%) | 0 (0.0%) | 8 (0.3%) | |
| 17081 | 60 (2.5%) | 0 (0.0%) | 0 (0.0%) | 60 (1.9%) | |
| 17918 | 21 (0.9%) | 0 (0.0%) | 0 (0.0%) | 21 (0.7%) | |
| 18755 | 136 (5.6%) | 0 (0.0%) | 0 (0.0%) | 136 (4.4%) | |
| 19034 | 8 (0.3%) | 0 (0.0%) | 0 (0.0%) | 8 (0.3%) | |
| 20677 | 13 (0.5%) | 0 (0.0%) | 0 (0.0%) | 13 (0.4%) | |
| 21080 | 182 (7.5%) | 0 (0.0%) | 0 (0.0%) | 182 (5.9%) | |
| 21514 | 6 (0.2%) | 0 (0.0%) | 0 (0.0%) | 6 (0.2%) | |
| 22692 | 50 (2.1%) | 0 (0.0%) | 0 (0.0%) | 50 (1.6%) | |
| 24304 | 232 (9.5%) | 0 (0.0%) | 0 (0.0%) | 232 (7.5%) | |
| 24335 | 9 (0.4%) | 0 (0.0%) | 0 (0.0%) | 9 (0.3%) | |
| 24521 | 14 (0.6%) | 0 (0.0%) | 0 (0.0%) | 14 (0.5%) | |
| 24800 | 71 (2.9%) | 0 (0.0%) | 0 (0.0%) | 71 (2.3%) | |
| 25644 | 1 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1 (0.0%) | |
| B | 0 (0.0%) | 0 (0.0%) | 32 (33.0%) | 32 (1.0%) | |
| C | 0 (0.0%) | 0 (0.0%) | 2 (2.1%) | 2 (0.1%) | |
| F | 0 (0.0%) | 0 (0.0%) | 8 (8.2%) | 8 (0.3%) | |
| G | 0 (0.0%) | 0 (0.0%) | 39 (40.2%) | 39 (1.3%) | |
| H | 0 (0.0%) | 0 (0.0%) | 12 (12.4%) | 12 (0.4%) | |
| J | 0 (0.0%) | 0 (0.0%) | 4 (4.1%) | 4 (0.1%) |
CAN <- bound3 %>% filter(COUNTRY == "CAN")
UK <- bound3 %>% filter(COUNTRY == "UK")
US <- bound3 %>% filter(COUNTRY == "US")
tab1 <- tableby(DTYPE ~
factor(TX_YR),
data=CAN, test=TRUE, total=TRUE,
numeric.stats=c("medianq1q3"), numeric.test="kwt", cat.test="chisq")
summary(tab1, title='Recipient characteristics', pfootnote=TRUE, digits = 2)
| DBD (N=2770) | DCD (N=300) | LDLT (N=556) | Total (N=3626) | p value | |
|---|---|---|---|---|---|
| factor(TX_YR) | < 0.0011 | ||||
| 2008 | 211 (7.6%) | 16 (5.3%) | 57 (10.3%) | 284 (7.8%) | |
| 2009 | 216 (7.8%) | 10 (3.3%) | 47 (8.5%) | 273 (7.5%) | |
| 2010 | 237 (8.6%) | 12 (4.0%) | 49 (8.8%) | 298 (8.2%) | |
| 2011 | 252 (9.1%) | 19 (6.3%) | 47 (8.5%) | 318 (8.8%) | |
| 2012 | 254 (9.2%) | 20 (6.7%) | 62 (11.2%) | 336 (9.3%) | |
| 2013 | 234 (8.4%) | 21 (7.0%) | 49 (8.8%) | 304 (8.4%) | |
| 2014 | 237 (8.6%) | 44 (14.7%) | 58 (10.4%) | 339 (9.3%) | |
| 2015 | 249 (9.0%) | 44 (14.7%) | 54 (9.7%) | 347 (9.6%) | |
| 2016 | 316 (11.4%) | 29 (9.7%) | 43 (7.7%) | 388 (10.7%) | |
| 2017 | 298 (10.8%) | 50 (16.7%) | 43 (7.7%) | 391 (10.8%) | |
| 2018 | 266 (9.6%) | 35 (11.7%) | 47 (8.5%) | 348 (9.6%) |
tab1 <- tableby(DTYPE ~
factor(TX_YR),
data=UK, test=TRUE, total=TRUE,
numeric.stats=c("medianq1q3"), numeric.test="kwt", cat.test="chisq")
summary(tab1, title='Recipient characteristics', pfootnote=TRUE, digits = 2)
| DBD (N=4937) | DCD (N=1561) | LDLT (N=97) | Total (N=6595) | p value | |
|---|---|---|---|---|---|
| factor(TX_YR) | < 0.0011 | ||||
| 2008 | 391 (7.9%) | 74 (4.7%) | 8 (8.2%) | 473 (7.2%) | |
| 2009 | 374 (7.6%) | 75 (4.8%) | 10 (10.3%) | 459 (7.0%) | |
| 2010 | 380 (7.7%) | 100 (6.4%) | 6 (6.2%) | 486 (7.4%) | |
| 2011 | 394 (8.0%) | 119 (7.6%) | 10 (10.3%) | 523 (7.9%) | |
| 2012 | 415 (8.4%) | 138 (8.8%) | 10 (10.3%) | 563 (8.5%) | |
| 2013 | 466 (9.4%) | 141 (9.0%) | 11 (11.3%) | 618 (9.4%) | |
| 2014 | 502 (10.2%) | 162 (10.4%) | 8 (8.2%) | 672 (10.2%) | |
| 2015 | 422 (8.5%) | 188 (12.0%) | 15 (15.5%) | 625 (9.5%) | |
| 2016 | 465 (9.4%) | 202 (12.9%) | 10 (10.3%) | 677 (10.3%) | |
| 2017 | 544 (11.0%) | 178 (11.4%) | 6 (6.2%) | 728 (11.0%) | |
| 2018 | 584 (11.8%) | 184 (11.8%) | 3 (3.1%) | 771 (11.7%) |
tab1 <- tableby(DTYPE ~
factor(TX_YR),
data=US, test=TRUE, total=TRUE,
numeric.stats=c("medianq1q3"), numeric.test="kwt", cat.test="chisq")
summary(tab1, title='Recipient characteristics', pfootnote=TRUE, digits = 2)
| DBD (N=54151) | DCD (N=3601) | LDLT (N=2433) | Total (N=60185) | p value | |
|---|---|---|---|---|---|
| factor(TX_YR) | < 0.0011 | ||||
| 2008 | 4501 (8.3%) | 257 (7.1%) | 162 (6.7%) | 4920 (8.2%) | |
| 2009 | 4554 (8.4%) | 256 (7.1%) | 148 (6.1%) | 4958 (8.2%) | |
| 2010 | 4516 (8.3%) | 244 (6.8%) | 196 (8.1%) | 4956 (8.2%) | |
| 2011 | 4626 (8.5%) | 244 (6.8%) | 176 (7.2%) | 5046 (8.4%) | |
| 2012 | 4555 (8.4%) | 243 (6.7%) | 181 (7.4%) | 4979 (8.3%) | |
| 2013 | 4625 (8.5%) | 287 (8.0%) | 200 (8.2%) | 5112 (8.5%) | |
| 2014 | 4786 (8.8%) | 325 (9.0%) | 213 (8.8%) | 5324 (8.8%) | |
| 2015 | 4977 (9.2%) | 368 (10.2%) | 267 (11.0%) | 5612 (9.3%) | |
| 2016 | 5522 (10.2%) | 402 (11.2%) | 274 (11.3%) | 6198 (10.3%) | |
| 2017 | 5659 (10.5%) | 478 (13.3%) | 288 (11.8%) | 6425 (10.7%) | |
| 2018 | 5830 (10.8%) | 497 (13.8%) | 328 (13.5%) | 6655 (11.1%) |
bound3 %>% select(DTYPE, DTYPE_DDLTandLDLT, GRAFT_TYPE) %>% View()
## Warning in system2("/usr/bin/otool", c("-L", shQuote(DSO)), stdout = TRUE):
## running command ''/usr/bin/otool' -L '/Library/Frameworks/R.framework/Resources/
## modules/R_de.so'' had status 1
Summaries for survival
#Overall
summary(tableby(COUNTRY ~ Surv(PSURV_years, PCENS), data=bound2, times=1:10, surv.stats=c("NeventsSurv", "NriskSurv")))
| US (N=2433) | CAN (N=556) | UK (N=97) | Total (N=3086) | p value | |
|---|---|---|---|---|---|
| Surv(PSURV_years, PCENS) | 0.001 | ||||
| time = 1 | 163 (93.0) | 23 (95.8) | 8 (91.4) | 194 (93.5) | |
| time = 2 | 221 (90.0) | 32 (93.9) | 9 (90.0) | 262 (90.8) | |
| time = 3 | 252 (88.1) | 40 (92.1) | 9 (90.0) | 301 (88.9) | |
| time = 4 | 284 (85.8) | 44 (91.1) | 10 (88.0) | 338 (86.9) | |
| time = 5 | 309 (83.5) | 48 (89.9) | 11 (85.4) | 368 (84.8) | |
| time = 6 | 333 (80.8) | 52 (88.4) | 11 (85.4) | 396 (82.5) | |
| time = 7 | 347 (78.9) | 53 (87.9) | 12 (80.0) | 412 (80.8) | |
| time = 8 | 351 (78.2) | 59 (84.4) | 12 (80.0) | 422 (79.5) | |
| time = 9 | 362 (75.3) | 61 (82.7) | 13 (66.7) | 436 (76.8) | |
| time = 10 | 373 (71.1) | 61 (82.7) | 13 (66.7) | 447 (73.5) | |
| time = 1 | 1979 (93.0) | 484 (95.8) | 79 (91.4) | 2542 (93.5) | |
| time = 2 | 1588 (90.0) | 434 (93.9) | 65 (90.0) | 2087 (90.8) | |
| time = 3 | 1313 (88.1) | 384 (92.1) | 50 (90.0) | 1747 (88.9) | |
| time = 4 | 1045 (85.8) | 329 (91.1) | 37 (88.0) | 1411 (86.9) | |
| time = 5 | 814 (83.5) | 272 (89.9) | 27 (85.4) | 1113 (84.8) | |
| time = 6 | 627 (80.8) | 225 (88.4) | 19 (85.4) | 871 (82.5) | |
| time = 7 | 485 (78.9) | 168 (87.9) | 12 (80.0) | 665 (80.8) | |
| time = 8 | 371 (78.2) | 123 (84.4) | 6 (80.0) | 500 (79.5) | |
| time = 9 | 229 (75.3) | 82 (82.7) | 4 (66.7) | 315 (76.8) | |
| time = 10 | 132 (71.1) | 43 (82.7) | 2 (66.7) | 177 (73.5) |
#1 year
summary(tableby(COUNTRY ~ Surv(PSURV_1year, PCENS_1year), data=bound2, times=1:1, surv.stats=c("NeventsSurv", "NriskSurv")))
| US (N=2433) | CAN (N=556) | UK (N=97) | Total (N=3086) | p value | |
|---|---|---|---|---|---|
| Surv(PSURV_1year, PCENS_1year) | 0.057 | ||||
| time = 1 | 163 (93.0) | 23 (95.8) | 8 (91.4) | 194 (93.5) | |
| time = 1 | 1979 (93.0) | 484 (95.8) | 79 (91.4) | 2542 (93.5) |
#3 year
summary(tableby(COUNTRY ~ Surv(PSURV_3year, PCENS_3year), data=bound2, times=1:3, surv.stats=c("NeventsSurv", "NriskSurv")))
| US (N=2433) | CAN (N=556) | UK (N=97) | Total (N=3086) | p value | |
|---|---|---|---|---|---|
| Surv(PSURV_3year, PCENS_3year) | 0.038 | ||||
| time = 1 | 163 (93.0) | 23 (95.8) | 8 (91.4) | 194 (93.5) | |
| time = 2 | 221 (90.0) | 32 (93.9) | 9 (90.0) | 262 (90.8) | |
| time = 3 | 252 (88.1) | 40 (92.1) | 9 (90.0) | 301 (88.9) | |
| time = 1 | 1979 (93.0) | 484 (95.8) | 79 (91.4) | 2542 (93.5) | |
| time = 2 | 1588 (90.0) | 434 (93.9) | 65 (90.0) | 2087 (90.8) | |
| time = 3 | 1313 (88.1) | 384 (92.1) | 50 (90.0) | 1747 (88.9) |
#5 year
summary(tableby(COUNTRY ~ Surv(PSURV_5year, PCENS_5year), data=bound2, times=1:5, surv.stats=c("NeventsSurv", "NriskSurv")))
| US (N=2433) | CAN (N=556) | UK (N=97) | Total (N=3086) | p value | |
|---|---|---|---|---|---|
| Surv(PSURV_5year, PCENS_5year) | 0.005 | ||||
| time = 1 | 163 (93.0) | 23 (95.8) | 8 (91.4) | 194 (93.5) | |
| time = 2 | 221 (90.0) | 32 (93.9) | 9 (90.0) | 262 (90.8) | |
| time = 3 | 252 (88.1) | 40 (92.1) | 9 (90.0) | 301 (88.9) | |
| time = 4 | 284 (85.8) | 44 (91.1) | 10 (88.0) | 338 (86.9) | |
| time = 5 | 309 (83.5) | 48 (89.9) | 11 (85.4) | 368 (84.8) | |
| time = 1 | 1979 (93.0) | 484 (95.8) | 79 (91.4) | 2542 (93.5) | |
| time = 2 | 1588 (90.0) | 434 (93.9) | 65 (90.0) | 2087 (90.8) | |
| time = 3 | 1313 (88.1) | 384 (92.1) | 50 (90.0) | 1747 (88.9) | |
| time = 4 | 1045 (85.8) | 329 (91.1) | 37 (88.0) | 1411 (86.9) | |
| time = 5 | 814 (83.5) | 272 (89.9) | 27 (85.4) | 1113 (84.8) |
#10 year
summary(tableby(COUNTRY ~ Surv(PSURV_10year, PCENS_10year), data=bound2, times=1:10, surv.stats=c("NeventsSurv", "NriskSurv")))
| US (N=2433) | CAN (N=556) | UK (N=97) | Total (N=3086) | p value | |
|---|---|---|---|---|---|
| Surv(PSURV_10year, PCENS_10year) | 0.001 | ||||
| time = 1 | 163 (93.0) | 23 (95.8) | 8 (91.4) | 194 (93.5) | |
| time = 2 | 221 (90.0) | 32 (93.9) | 9 (90.0) | 262 (90.8) | |
| time = 3 | 252 (88.1) | 40 (92.1) | 9 (90.0) | 301 (88.9) | |
| time = 4 | 284 (85.8) | 44 (91.1) | 10 (88.0) | 338 (86.9) | |
| time = 5 | 309 (83.5) | 48 (89.9) | 11 (85.4) | 368 (84.8) | |
| time = 6 | 333 (80.8) | 52 (88.4) | 11 (85.4) | 396 (82.5) | |
| time = 7 | 347 (78.9) | 53 (87.9) | 12 (80.0) | 412 (80.8) | |
| time = 8 | 351 (78.2) | 59 (84.4) | 12 (80.0) | 422 (79.5) | |
| time = 9 | 362 (75.3) | 61 (82.7) | 13 (66.7) | 436 (76.8) | |
| time = 10 | 373 (71.1) | 61 (82.7) | 13 (66.7) | 447 (73.5) | |
| time = 1 | 1979 (93.0) | 484 (95.8) | 79 (91.4) | 2542 (93.5) | |
| time = 2 | 1588 (90.0) | 434 (93.9) | 65 (90.0) | 2087 (90.8) | |
| time = 3 | 1313 (88.1) | 384 (92.1) | 50 (90.0) | 1747 (88.9) | |
| time = 4 | 1045 (85.8) | 329 (91.1) | 37 (88.0) | 1411 (86.9) | |
| time = 5 | 814 (83.5) | 272 (89.9) | 27 (85.4) | 1113 (84.8) | |
| time = 6 | 627 (80.8) | 225 (88.4) | 19 (85.4) | 871 (82.5) | |
| time = 7 | 485 (78.9) | 168 (87.9) | 12 (80.0) | 665 (80.8) | |
| time = 8 | 371 (78.2) | 123 (84.4) | 6 (80.0) | 500 (79.5) | |
| time = 9 | 229 (75.3) | 82 (82.7) | 4 (66.7) | 315 (76.8) | |
| time = 10 | 132 (71.1) | 43 (82.7) | 2 (66.7) | 177 (73.5) |
#Median
survfit(Surv(PSURV_years, PCENS) ~ COUNTRY, data = bound2)
Call: survfit(formula = Surv(PSURV_years, PCENS) ~ COUNTRY, data = bound2)
n events median 0.95LCL 0.95UCL
COUNTRY=US 2433 377 NA NA NA COUNTRY=CAN 556 61 NA NA NA COUNTRY=UK 97 13 NA 8.13 NA
summary(survfit(Surv(PSURV_years, PCENS)~COUNTRY, data=bound2), times=1:10)
Call: survfit(formula = Surv(PSURV_years, PCENS) ~ COUNTRY, data = bound2)
COUNTRY=US
time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 1979 163 0.930 0.00528 0.920 0.941 2 1588 58 0.900 0.00640 0.888 0.913 3 1313 31 0.881 0.00712 0.868 0.895 4 1045 32 0.858 0.00805 0.842 0.874 5 814 25 0.835 0.00902 0.818 0.853 6 627 24 0.808 0.01027 0.788 0.829 7 485 14 0.789 0.01127 0.767 0.811 8 371 4 0.782 0.01173 0.759 0.805 9 229 11 0.753 0.01412 0.726 0.781 10 132 11 0.711 0.01823 0.676 0.748
COUNTRY=CAN
time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 484 23 0.958 0.00867 0.941 0.975 2 434 9 0.939 0.01045 0.919 0.960 3 384 8 0.921 0.01204 0.898 0.945 4 329 4 0.911 0.01300 0.886 0.936 5 272 4 0.899 0.01415 0.871 0.927 6 225 4 0.884 0.01571 0.854 0.915 7 168 1 0.879 0.01646 0.847 0.912 8 123 6 0.844 0.02110 0.804 0.886 9 82 2 0.827 0.02385 0.782 0.875 10 43 0 0.827 0.02385 0.782 0.875
COUNTRY=UK
time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 79 8 0.914 0.0293 0.858 0.973 2 65 1 0.900 0.0317 0.840 0.965 3 50 0 0.900 0.0317 0.840 0.965 4 37 1 0.880 0.0368 0.811 0.955 5 27 1 0.854 0.0443 0.771 0.945 6 19 0 0.854 0.0443 0.771 0.945 7 12 1 0.800 0.0663 0.680 0.941 8 6 0 0.800 0.0663 0.680 0.941 9 4 1 0.667 0.1337 0.450 0.988 10 2 0 0.667 0.1337 0.450 0.988
fit4 <- pairwise_survdiff(Surv(PSURV_years, PCENS) ~ COUNTRY , data = bound2)
fit4
Pairwise comparisons using Log-Rank test
data: bound2 and COUNTRY
US CAN
CAN 0.00066 -
UK 0.86368 0.19571
P value adjustment method: BH
symnum(fit4$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
symbols = c("****", "***", "**", "*", "+", " "),
abbr.colnames = FALSE, na = "")
US CAN
CAN ***
UK
attr(,“legend”) [1] 0 ‘****’ 0.0001 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘+’ 0.1 ’ ’ 1 \t ## NA: ’’
Summaries for graft survival
#Overall
summary(tableby(COUNTRY ~ Surv(GSURV_years, GCENS), data=bound2, times=1:10, surv.stats=c("NeventsSurv", "NriskSurv")))
| US (N=2433) | CAN (N=556) | UK (N=97) | Total (N=3086) | p value | |
|---|---|---|---|---|---|
| Surv(GSURV_years, GCENS) | < 0.001 | ||||
| time = 1 | 282 (88.4) | 39 (92.8) | 11 (88.2) | 332 (89.1) | |
| time = 2 | 353 (84.9) | 49 (90.8) | 12 (86.7) | 414 (86.0) | |
| time = 3 | 387 (83.0) | 55 (89.4) | 12 (86.7) | 454 (84.2) | |
| time = 4 | 425 (80.3) | 61 (87.8) | 12 (86.7) | 498 (81.8) | |
| time = 5 | 455 (77.8) | 62 (87.4) | 12 (86.7) | 529 (79.8) | |
| time = 6 | 481 (75.0) | 64 (86.7) | 12 (86.7) | 557 (77.5) | |
| time = 7 | 497 (73.0) | 66 (85.8) | 13 (81.3) | 576 (75.7) | |
| time = 8 | 505 (71.6) | 69 (83.8) | 13 (81.3) | 587 (74.2) | |
| time = 9 | 517 (68.8) | 72 (81.1) | 13 (81.3) | 602 (71.4) | |
| time = 10 | 529 (64.6) | 72 (81.1) | 13 (81.3) | 614 (68.0) | |
| time = 1 | 1979 (88.4) | 465 (92.8) | 71 (88.2) | 2515 (89.1) | |
| time = 2 | 1587 (84.9) | 412 (90.8) | 56 (86.7) | 2055 (86.0) | |
| time = 3 | 1313 (83.0) | 362 (89.4) | 42 (86.7) | 1717 (84.2) | |
| time = 4 | 1044 (80.3) | 305 (87.8) | 32 (86.7) | 1381 (81.8) | |
| time = 5 | 815 (77.8) | 250 (87.4) | 23 (86.7) | 1088 (79.8) | |
| time = 6 | 627 (75.0) | 210 (86.7) | 17 (86.7) | 854 (77.5) | |
| time = 7 | 485 (73.0) | 156 (85.8) | 9 (81.3) | 650 (75.7) | |
| time = 8 | 371 (71.6) | 114 (83.8) | 6 (81.3) | 491 (74.2) | |
| time = 9 | 228 (68.8) | 74 (81.1) | 4 (81.3) | 306 (71.4) | |
| time = 10 | 131 (64.6) | 38 (81.1) | 2 (81.3) | 171 (68.0) |
#1 year
summary(tableby(COUNTRY ~ Surv(GSURV_1year, GCENS_1year), data=bound2, times=1:1, surv.stats=c("NeventsSurv", "NriskSurv")))
| US (N=2433) | CAN (N=556) | UK (N=97) | Total (N=3086) | p value | |
|---|---|---|---|---|---|
| Surv(GSURV_1year, GCENS_1year) | 0.015 | ||||
| time = 1 | 282 (88.4) | 39 (92.8) | 11 (88.2) | 332 (89.1) | |
| time = 1 | 1979 (88.4) | 465 (92.8) | 71 (88.2) | 2515 (89.1) |
#3 year
summary(tableby(COUNTRY ~ Surv(GSURV_3year, GCENS_3year), data=bound2, times=1:3, surv.stats=c("NeventsSurv", "NriskSurv")))
| US (N=2433) | CAN (N=556) | UK (N=97) | Total (N=3086) | p value | |
|---|---|---|---|---|---|
| Surv(GSURV_3year, GCENS_3year) | 0.002 | ||||
| time = 1 | 282 (88.4) | 39 (92.8) | 11 (88.2) | 332 (89.1) | |
| time = 2 | 353 (84.9) | 49 (90.8) | 12 (86.7) | 414 (86.0) | |
| time = 3 | 387 (83.0) | 55 (89.4) | 12 (86.7) | 454 (84.2) | |
| time = 1 | 1979 (88.4) | 465 (92.8) | 71 (88.2) | 2515 (89.1) | |
| time = 2 | 1587 (84.9) | 412 (90.8) | 56 (86.7) | 2055 (86.0) | |
| time = 3 | 1313 (83.0) | 362 (89.4) | 42 (86.7) | 1717 (84.2) |
#5 year
summary(tableby(COUNTRY ~ Surv(GSURV_5year, GCENS_5year), data=bound2, times=1:5, surv.stats=c("NeventsSurv", "NriskSurv")))
| US (N=2433) | CAN (N=556) | UK (N=97) | Total (N=3086) | p value | |
|---|---|---|---|---|---|
| Surv(GSURV_5year, GCENS_5year) | < 0.001 | ||||
| time = 1 | 282 (88.4) | 39 (92.8) | 11 (88.2) | 332 (89.1) | |
| time = 2 | 353 (84.9) | 49 (90.8) | 12 (86.7) | 414 (86.0) | |
| time = 3 | 387 (83.0) | 55 (89.4) | 12 (86.7) | 454 (84.2) | |
| time = 4 | 425 (80.3) | 61 (87.8) | 12 (86.7) | 498 (81.8) | |
| time = 5 | 455 (77.8) | 62 (87.4) | 12 (86.7) | 529 (79.8) | |
| time = 1 | 1979 (88.4) | 465 (92.8) | 71 (88.2) | 2515 (89.1) | |
| time = 2 | 1587 (84.9) | 412 (90.8) | 56 (86.7) | 2055 (86.0) | |
| time = 3 | 1313 (83.0) | 362 (89.4) | 42 (86.7) | 1717 (84.2) | |
| time = 4 | 1044 (80.3) | 305 (87.8) | 32 (86.7) | 1381 (81.8) | |
| time = 5 | 815 (77.8) | 250 (87.4) | 23 (86.7) | 1088 (79.8) |
#10 year
summary(tableby(COUNTRY ~ Surv(GSURV_10year, GCENS_10year), data=bound2, times=1:10, surv.stats=c("NeventsSurv", "NriskSurv")))
| US (N=2433) | CAN (N=556) | UK (N=97) | Total (N=3086) | p value | |
|---|---|---|---|---|---|
| Surv(GSURV_10year, GCENS_10year) | < 0.001 | ||||
| time = 1 | 282 (88.4) | 39 (92.8) | 11 (88.2) | 332 (89.1) | |
| time = 2 | 353 (84.9) | 49 (90.8) | 12 (86.7) | 414 (86.0) | |
| time = 3 | 387 (83.0) | 55 (89.4) | 12 (86.7) | 454 (84.2) | |
| time = 4 | 425 (80.3) | 61 (87.8) | 12 (86.7) | 498 (81.8) | |
| time = 5 | 455 (77.8) | 62 (87.4) | 12 (86.7) | 529 (79.8) | |
| time = 6 | 481 (75.0) | 64 (86.7) | 12 (86.7) | 557 (77.5) | |
| time = 7 | 497 (73.0) | 66 (85.8) | 13 (81.3) | 576 (75.7) | |
| time = 8 | 505 (71.6) | 69 (83.8) | 13 (81.3) | 587 (74.2) | |
| time = 9 | 517 (68.8) | 72 (81.1) | 13 (81.3) | 602 (71.4) | |
| time = 10 | 529 (64.6) | 72 (81.1) | 13 (81.3) | 614 (68.0) | |
| time = 1 | 1979 (88.4) | 465 (92.8) | 71 (88.2) | 2515 (89.1) | |
| time = 2 | 1587 (84.9) | 412 (90.8) | 56 (86.7) | 2055 (86.0) | |
| time = 3 | 1313 (83.0) | 362 (89.4) | 42 (86.7) | 1717 (84.2) | |
| time = 4 | 1044 (80.3) | 305 (87.8) | 32 (86.7) | 1381 (81.8) | |
| time = 5 | 815 (77.8) | 250 (87.4) | 23 (86.7) | 1088 (79.8) | |
| time = 6 | 627 (75.0) | 210 (86.7) | 17 (86.7) | 854 (77.5) | |
| time = 7 | 485 (73.0) | 156 (85.8) | 9 (81.3) | 650 (75.7) | |
| time = 8 | 371 (71.6) | 114 (83.8) | 6 (81.3) | 491 (74.2) | |
| time = 9 | 228 (68.8) | 74 (81.1) | 4 (81.3) | 306 (71.4) | |
| time = 10 | 131 (64.6) | 38 (81.1) | 2 (81.3) | 171 (68.0) |
#Median
survfit(Surv(GSURV_years, GCENS) ~ COUNTRY, data = bound2)
Call: survfit(formula = Surv(GSURV_years, GCENS) ~ COUNTRY, data = bound2)
n events median 0.95LCL 0.95UCL
COUNTRY=US 2433 537 NA 11.1 NA COUNTRY=CAN 556 72 NA NA NA COUNTRY=UK 97 13 NA NA NA
summary(survfit(Surv(GSURV_years, GCENS)~COUNTRY, data=bound2), times=1:10)
Call: survfit(formula = Surv(GSURV_years, GCENS) ~ COUNTRY, data = bound2)
COUNTRY=US
time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 1979 282 0.884 0.00652 0.871 0.896 2 1587 71 0.849 0.00744 0.835 0.864 3 1313 34 0.830 0.00799 0.814 0.845 4 1044 38 0.803 0.00881 0.786 0.821 5 815 30 0.778 0.00969 0.759 0.797 6 627 26 0.750 0.01072 0.730 0.772 7 485 16 0.730 0.01161 0.707 0.753 8 371 8 0.716 0.01235 0.692 0.741 9 228 12 0.688 0.01430 0.661 0.717 10 131 12 0.646 0.01795 0.611 0.682
COUNTRY=CAN
time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 465 39 0.928 0.0111 0.907 0.950 2 412 10 0.908 0.0126 0.883 0.933 3 362 6 0.894 0.0136 0.868 0.921 4 305 6 0.878 0.0149 0.849 0.907 5 250 1 0.874 0.0152 0.845 0.905 6 210 2 0.867 0.0160 0.836 0.899 7 156 2 0.858 0.0171 0.825 0.892 8 114 3 0.838 0.0200 0.800 0.879 9 74 3 0.811 0.0249 0.764 0.861 10 38 0 0.811 0.0249 0.764 0.861
COUNTRY=UK
time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 71 11 0.882 0.0336 0.818 0.950 2 56 1 0.867 0.0360 0.800 0.941 3 42 0 0.867 0.0360 0.800 0.941 4 32 0 0.867 0.0360 0.800 0.941 5 23 0 0.867 0.0360 0.800 0.941 6 17 0 0.867 0.0360 0.800 0.941 7 9 1 0.813 0.0624 0.700 0.945 8 6 0 0.813 0.0624 0.700 0.945 9 4 0 0.813 0.0624 0.700 0.945 10 2 0 0.813 0.0624 0.700 0.945
fit4 <- pairwise_survdiff(Surv(GSURV_years, GCENS) ~ COUNTRY , data = bound2)
fit4
Pairwise comparisons using Log-Rank test
data: bound2 and COUNTRY
US CAN
CAN 0.0000018 -
UK 0.35 0.45
P value adjustment method: BH
symnum(fit4$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
symbols = c("****", "***", "**", "*", "+", " "),
abbr.colnames = FALSE, na = "")
US CAN
CAN ****
UK
attr(,“legend”) [1] 0 ‘****’ 0.0001 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘+’ 0.1 ’ ’ 1 \t ## NA: ’’
OS overall Figure 3
levels(bound2$COUNTRY)
[1] “US” “CAN” “UK”
fit1 <- survfit(Surv(PSURV_years, PCENS) ~ COUNTRY, data = bound2)
ggsurv1 <- ggsurvplot(
fit1,
data = bound2,
risk.table = TRUE,
pval = TRUE,
pval.size = 6,
pval.coord = c(0, 0.8),
conf.int = F,
xlim = c(0,5),
ylim = c(0.7, 1.00),
xlab = "Years from LT",
palette = "jama",
break.time.by = 1,
title = "",
ggtheme = theme_test(base_size=28, base_family = "Helvetica"),
fontsize=8,
risk.table.height = 0.25,
tables.x.text = FALSE,
legend.title= "",
legend.labs =
c("US", "Canada", "UK"),
)
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
ggsurv1$table <- ggsurv1$table + labs(x = NULL, y = NULL) + theme(plot.title = element_text(size = 28))
ggsurv1$plot <- ggsurv1$plot+
ggplot2::annotate("text",
x = 2.5, y = 0.7, # x and y coordinates of the text
label = "Adjusted HR (ref: US, Canada HR 0.53, 95% CI 0.25-1.14, UK HR 1.34, 95% CI 0.71-2.53)", size = 6)
ggsurv1
## Warning: Removed 72 row(s) containing missing values (geom_path).
## Warning: Removed 69 rows containing missing values (geom_point).
## Warning: Removed 72 row(s) containing missing values (geom_path).
## Warning: Removed 69 rows containing missing values (geom_point).
fit4 <- pairwise_survdiff(Surv(PSURV_years, PCENS) ~ COUNTRY, data = bound2)
fit4
Pairwise comparisons using Log-Rank test
data: bound2 and COUNTRY
US CAN
CAN 0.00066 -
UK 0.86368 0.19571
P value adjustment method: BH
symnum(fit4$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
symbols = c("****", "***", "**", "*", "+", " "),
abbr.colnames = FALSE, na = "")
US CAN
CAN ***
UK
attr(,“legend”) [1] 0 ‘****’ 0.0001 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘+’ 0.1 ’ ’ 1 \t ## NA: ’’
Graft survival overall Figure S1
levels(bound2$COUNTRY)
[1] “US” “CAN” “UK”
fit1 <- survfit(Surv(GSURV_years, GCENS) ~ COUNTRY, data = bound2)
ggsurv1 <- ggsurvplot(
fit1,
data = bound2,
risk.table = TRUE,
pval = TRUE,
pval.size = 6,
pval.coord = c(0, 0.8),
conf.int = F,
xlim = c(0,5),
ylim = c(0.7, 1.00),
xlab = "Years from LT",
palette = "jama",
break.time.by = 1,
title = "",
ggtheme = theme_test(base_size=28, base_family = "Helvetica"),
fontsize=8,
risk.table.height = 0.25,
tables.x.text = FALSE,
legend.title= "",
legend.labs =
c("US", "Canada", "UK"),
)
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
ggsurv1$table <- ggsurv1$table + labs(x = NULL, y = NULL) + theme(plot.title = element_text(size = 28))
ggsurv1$plot <- ggsurv1$plot+
ggplot2::annotate("text",
x = 2.5, y = 0.7, # x and y coordinates of the text
label = "Adjusted HR (ref: US, Canada HR 0.54, 95% CI 0.29-1.01, UK HR 0.91, 95% CI 0.49-1.70)", size = 6)
ggsurv1
## Warning: Removed 219 row(s) containing missing values (geom_path).
## Warning: Removed 199 rows containing missing values (geom_point).
## Warning: Removed 219 row(s) containing missing values (geom_path).
## Warning: Removed 199 rows containing missing values (geom_point).
fit4 <- pairwise_survdiff(Surv(GSURV_years, GCENS) ~ COUNTRY, data = bound2)
fit4
Pairwise comparisons using Log-Rank test
data: bound2 and COUNTRY
US CAN
CAN 0.0000018 -
UK 0.35 0.45
P value adjustment method: BH
symnum(fit4$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
symbols = c("****", "***", "**", "*", "+", " "),
abbr.colnames = FALSE, na = "")
US CAN
CAN ****
UK
attr(,“legend”) [1] 0 ‘****’ 0.0001 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘+’ 0.1 ’ ’ 1 \t ## NA: ’’
OS overall by percentile
levels(bound2$COUNTRY)
[1] “US” “CAN” “UK”
fit1 <- survfit(Surv(PSURV_years, PCENS) ~ Centerpercentile, data = bound2)
ggsurv1 <- ggsurvplot(
fit1,
data = bound2,
risk.table = TRUE,
pval = TRUE,
pval.size = 6,
pval.coord = c(0, 0.8),
conf.int = F,
xlim = c(0,5),
ylim = c(0.7, 1.00),
xlab = "Years from LT",
palette = "jama",
break.time.by = 1,
title = "",
ggtheme = theme_test(base_size=28, base_family = "Helvetica"),
fontsize=8,
risk.table.height = 0.25,
tables.x.text = FALSE,
legend.title= "",
legend.labs =
c("<25%ile", "25%-75%ile", ">75%ile"),
)
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
ggsurv1
## Warning: Removed 28 row(s) containing missing values (geom_path).
## Warning: Removed 27 rows containing missing values (geom_point).
## Warning: Removed 28 row(s) containing missing values (geom_path).
## Warning: Removed 27 rows containing missing values (geom_point).
OS overall DDLT vs LDLT US Figure 4a
US <- bound3 %>% filter(COUNTRY == "US")
fit1 <- survfit(Surv(PSURV_years, PCENS) ~ LDLT, data = US)
ggsurv1 <- ggsurvplot(
fit1,
data = US,
risk.table = TRUE,
pval = TRUE,
pval.coord = c(0, 0.8),
pval.size = 6,
conf.int = F,
xlim = c(0,5),
ylim = c(0.5, 1.00),
xlab = "Years from LT",
palette = "jama",
break.time.by = 1,
title = "United States",
# subtitle = "with 95% confidence intervals",
ggtheme = theme_test(base_size=28, base_family = "Helvetica"),
# risk.table.y.text.col = F,
fontsize=8,
risk.table.height = 0.25,
# risk.table.y.text = T,
# surv.median.line = "hv",
legend.title= "",
tables.x.text = FALSE,
legend.labs =
c("DDLT", "LDLT")
)
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
ggsurv1
ggsurv1$plot <- ggsurv1$plot+
ggplot2::annotate("text",
x = 2.5, y = 0.5, # x and y coordinates of the text
label = "Adjusted HR (ref: DDLT, HR LDLT 1.03, 95% CI 0.92-1.16)", size = 6)
ggsurv1
#Overall
summary(tableby(LDLT ~ Surv(PSURV_years, PCENS), data=US, times=1:10, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=57752) | 1 (N=2433) | Total (N=60185) | p value | |
|---|---|---|---|---|
| Surv(PSURV_years, PCENS) | < 0.001 | |||
| time = 1 | 4795 (91.5) | 163 (93.0) | 4958 (91.6) | |
| time = 2 | 6758 (87.6) | 221 (90.0) | 6979 (87.7) | |
| time = 3 | 8194 (84.2) | 252 (88.1) | 8446 (84.4) | |
| time = 4 | 9275 (81.2) | 284 (85.8) | 9559 (81.4) | |
| time = 5 | 10208 (78.2) | 309 (83.5) | 10517 (78.4) | |
| time = 6 | 10935 (75.3) | 333 (80.8) | 11268 (75.5) | |
| time = 7 | 11509 (72.5) | 347 (78.9) | 11856 (72.8) | |
| time = 8 | 11947 (69.7) | 351 (78.2) | 12298 (70.0) | |
| time = 9 | 12284 (66.8) | 362 (75.3) | 12646 (67.1) | |
| time = 10 | 12543 (63.3) | 373 (71.1) | 12916 (63.5) | |
| time = 1 | 48435 (91.5) | 1979 (93.0) | 50414 (91.6) | |
| time = 2 | 40076 (87.6) | 1588 (90.0) | 41664 (87.7) | |
| time = 3 | 32852 (84.2) | 1313 (88.1) | 34165 (84.4) | |
| time = 4 | 26844 (81.2) | 1045 (85.8) | 27889 (81.4) | |
| time = 5 | 21520 (78.2) | 814 (83.5) | 22334 (78.4) | |
| time = 6 | 16843 (75.3) | 627 (80.8) | 17470 (75.5) | |
| time = 7 | 12795 (72.5) | 485 (78.9) | 13280 (72.8) | |
| time = 8 | 9153 (69.7) | 371 (78.2) | 9524 (70.0) | |
| time = 9 | 5899 (66.8) | 229 (75.3) | 6128 (67.1) | |
| time = 10 | 3126 (63.3) | 132 (71.1) | 3258 (63.5) |
#1 year
summary(tableby(LDLT ~ Surv(PSURV_1year, PCENS_1year), data=US, times=1:1, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=57752) | 1 (N=2433) | Total (N=60185) | p value | |
|---|---|---|---|---|
| Surv(PSURV_1year, PCENS_1year) | 0.010 | |||
| time = 1 | 4795 (91.5) | 163 (93.0) | 4958 (91.6) | |
| time = 1 | 48435 (91.5) | 1979 (93.0) | 50414 (91.6) |
#3 year
summary(tableby(LDLT ~ Surv(PSURV_3year, PCENS_3year), data=US, times=1:3, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=57752) | 1 (N=2433) | Total (N=60185) | p value | |
|---|---|---|---|---|
| Surv(PSURV_3year, PCENS_3year) | < 0.001 | |||
| time = 1 | 4795 (91.5) | 163 (93.0) | 4958 (91.6) | |
| time = 2 | 6758 (87.6) | 221 (90.0) | 6979 (87.7) | |
| time = 3 | 8194 (84.2) | 252 (88.1) | 8446 (84.4) | |
| time = 1 | 48435 (91.5) | 1979 (93.0) | 50414 (91.6) | |
| time = 2 | 40076 (87.6) | 1588 (90.0) | 41664 (87.7) | |
| time = 3 | 32852 (84.2) | 1313 (88.1) | 34165 (84.4) |
#5 year
summary(tableby(LDLT ~ Surv(PSURV_5year, PCENS_5year), data=US, times=1:5, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=57752) | 1 (N=2433) | Total (N=60185) | p value | |
|---|---|---|---|---|
| Surv(PSURV_5year, PCENS_5year) | < 0.001 | |||
| time = 1 | 4795 (91.5) | 163 (93.0) | 4958 (91.6) | |
| time = 2 | 6758 (87.6) | 221 (90.0) | 6979 (87.7) | |
| time = 3 | 8194 (84.2) | 252 (88.1) | 8446 (84.4) | |
| time = 4 | 9275 (81.2) | 284 (85.8) | 9559 (81.4) | |
| time = 5 | 10208 (78.2) | 309 (83.5) | 10517 (78.4) | |
| time = 1 | 48435 (91.5) | 1979 (93.0) | 50414 (91.6) | |
| time = 2 | 40076 (87.6) | 1588 (90.0) | 41664 (87.7) | |
| time = 3 | 32852 (84.2) | 1313 (88.1) | 34165 (84.4) | |
| time = 4 | 26844 (81.2) | 1045 (85.8) | 27889 (81.4) | |
| time = 5 | 21520 (78.2) | 814 (83.5) | 22334 (78.4) |
#10 year
summary(tableby(LDLT ~ Surv(PSURV_10year, PCENS_10year), data=US, times=1:10, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=57752) | 1 (N=2433) | Total (N=60185) | p value | |
|---|---|---|---|---|
| Surv(PSURV_10year, PCENS_10year) | < 0.001 | |||
| time = 1 | 4795 (91.5) | 163 (93.0) | 4958 (91.6) | |
| time = 2 | 6758 (87.6) | 221 (90.0) | 6979 (87.7) | |
| time = 3 | 8194 (84.2) | 252 (88.1) | 8446 (84.4) | |
| time = 4 | 9275 (81.2) | 284 (85.8) | 9559 (81.4) | |
| time = 5 | 10208 (78.2) | 309 (83.5) | 10517 (78.4) | |
| time = 6 | 10935 (75.3) | 333 (80.8) | 11268 (75.5) | |
| time = 7 | 11509 (72.5) | 347 (78.9) | 11856 (72.8) | |
| time = 8 | 11947 (69.7) | 351 (78.2) | 12298 (70.0) | |
| time = 9 | 12284 (66.8) | 362 (75.3) | 12646 (67.1) | |
| time = 10 | 12543 (63.3) | 373 (71.1) | 12916 (63.5) | |
| time = 1 | 48435 (91.5) | 1979 (93.0) | 50414 (91.6) | |
| time = 2 | 40076 (87.6) | 1588 (90.0) | 41664 (87.7) | |
| time = 3 | 32852 (84.2) | 1313 (88.1) | 34165 (84.4) | |
| time = 4 | 26844 (81.2) | 1045 (85.8) | 27889 (81.4) | |
| time = 5 | 21520 (78.2) | 814 (83.5) | 22334 (78.4) | |
| time = 6 | 16843 (75.3) | 627 (80.8) | 17470 (75.5) | |
| time = 7 | 12795 (72.5) | 485 (78.9) | 13280 (72.8) | |
| time = 8 | 9153 (69.7) | 371 (78.2) | 9524 (70.0) | |
| time = 9 | 5899 (66.8) | 229 (75.3) | 6128 (67.1) | |
| time = 10 | 3126 (63.3) | 132 (71.1) | 3258 (63.5) |
#Median
survfit(Surv(PSURV_years, PCENS) ~ LDLT, data = US)
Call: survfit(formula = Surv(PSURV_years, PCENS) ~ LDLT, data = US)
n events median 0.95LCL 0.95UCL
LDLT=0 57752 12708 NA 12 NA LDLT=1 2433 377 NA NA NA
summary(survfit(Surv(PSURV_years, PCENS)~LDLT, data=US), times=1:10)
Call: survfit(formula = Surv(PSURV_years, PCENS) ~ LDLT, data = US)
LDLT=0
time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 48435 4795 0.915 0.00117 0.913 0.918 2 40076 1963 0.876 0.00142 0.873 0.879 3 32852 1436 0.842 0.00162 0.839 0.845 4 26844 1081 0.812 0.00180 0.809 0.816 5 21520 933 0.782 0.00199 0.778 0.786 6 16843 727 0.753 0.00218 0.749 0.758 7 12795 574 0.725 0.00240 0.721 0.730 8 9153 438 0.697 0.00265 0.692 0.703 9 5899 337 0.668 0.00299 0.662 0.674 10 3126 259 0.633 0.00355 0.626 0.640
LDLT=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 1979 163 0.930 0.00528 0.920 0.941 2 1588 58 0.900 0.00640 0.888 0.913 3 1313 31 0.881 0.00712 0.868 0.895 4 1045 32 0.858 0.00805 0.842 0.874 5 814 25 0.835 0.00902 0.818 0.853 6 627 24 0.808 0.01027 0.788 0.829 7 485 14 0.789 0.01127 0.767 0.811 8 371 4 0.782 0.01173 0.759 0.805 9 229 11 0.753 0.01412 0.726 0.781 10 132 11 0.711 0.01823 0.676 0.748
fit4 <- pairwise_survdiff(Surv(PSURV_years, PCENS) ~ LDLT , data = US)
fit4
Pairwise comparisons using Log-Rank test
data: US and LDLT
0
1 0.0000000041
P value adjustment method: BH
symnum(fit4$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
symbols = c("****", "***", "**", "*", "+", " "),
abbr.colnames = FALSE, na = "")
0
1 **** attr(,“legend”) [1] 0 ‘****’ 0.0001 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘+’ 0.1 ’ ’ 1 OS overall DDLT vs LDLT CAN Fig 4b
CAN <- bound3 %>% filter(COUNTRY == "CAN")
fit1 <- survfit(Surv(PSURV_years, PCENS) ~ LDLT, data = CAN)
ggsurv1 <- ggsurvplot(
fit1,
data = CAN,
risk.table = TRUE,
pval = TRUE,
pval.coord = c(0, 0.8),
pval.size = 6,
conf.int = F,
xlim = c(0,5),
ylim = c(0.5, 1.00),
xlab = "Years from LT",
palette = "jama",
break.time.by = 1,
title = "Canada",
# subtitle = "with 95% confidence intervals",
ggtheme = theme_test(base_size=28, base_family = "Helvetica"),
# risk.table.y.text.col = F,
fontsize=8,
risk.table.height = 0.25,
# risk.table.y.text = T,
# surv.median.line = "hv",
legend.title= "",
tables.x.text = FALSE,
legend.labs =
c("DDLT", "LDLT")
)
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
ggsurv1
ggsurv1$plot <- ggsurv1$plot+
ggplot2::annotate("text",
x = 2.5, y = 0.5, # x and y coordinates of the text
label = "Adjusted HR (ref: DDLT, HR LDLT 0.65, 95% CI 0.30-1.41)", size = 6)
ggsurv1
#Overall
summary(tableby(LDLT ~ Surv(PSURV_years, PCENS), data=CAN, times=1:10, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=3070) | 1 (N=556) | Total (N=3626) | p value | |
|---|---|---|---|---|
| Surv(PSURV_years, PCENS) | < 0.001 | |||
| time = 1 | 250 (91.6) | 23 (95.8) | 273 (92.2) | |
| time = 2 | 332 (88.4) | 32 (93.9) | 364 (89.3) | |
| time = 3 | 378 (86.4) | 40 (92.1) | 418 (87.3) | |
| time = 4 | 414 (84.5) | 44 (91.1) | 458 (85.5) | |
| time = 5 | 444 (82.6) | 48 (89.9) | 492 (83.8) | |
| time = 6 | 462 (81.3) | 52 (88.4) | 514 (82.4) | |
| time = 7 | 486 (79.2) | 53 (87.9) | 539 (80.6) | |
| time = 8 | 498 (77.6) | 59 (84.4) | 557 (78.7) | |
| time = 9 | 504 (76.5) | 61 (82.7) | 565 (77.5) | |
| time = 10 | 508 (75.4) | 61 (82.7) | 569 (76.5) | |
| time = 1 | 2528 (91.6) | 484 (95.8) | 3012 (92.2) | |
| time = 2 | 2124 (88.4) | 434 (93.9) | 2558 (89.3) | |
| time = 3 | 1770 (86.4) | 384 (92.1) | 2154 (87.3) | |
| time = 4 | 1495 (84.5) | 329 (91.1) | 1824 (85.5) | |
| time = 5 | 1234 (82.6) | 272 (89.9) | 1506 (83.8) | |
| time = 6 | 1018 (81.3) | 225 (88.4) | 1243 (82.4) | |
| time = 7 | 763 (79.2) | 168 (87.9) | 931 (80.6) | |
| time = 8 | 536 (77.6) | 123 (84.4) | 659 (78.7) | |
| time = 9 | 350 (76.5) | 82 (82.7) | 432 (77.5) | |
| time = 10 | 178 (75.4) | 43 (82.7) | 221 (76.5) |
#1 year
summary(tableby(LDLT ~ Surv(PSURV_1year, PCENS_1year), data=CAN, times=1:1, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=3070) | 1 (N=556) | Total (N=3626) | p value | |
|---|---|---|---|---|
| Surv(PSURV_1year, PCENS_1year) | 0.001 | |||
| time = 1 | 250 (91.6) | 23 (95.8) | 273 (92.2) | |
| time = 1 | 2528 (91.6) | 484 (95.8) | 3012 (92.2) |
#3 year
summary(tableby(LDLT ~ Surv(PSURV_3year, PCENS_3year), data=CAN, times=1:3, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=3070) | 1 (N=556) | Total (N=3626) | p value | |
|---|---|---|---|---|
| Surv(PSURV_3year, PCENS_3year) | < 0.001 | |||
| time = 1 | 250 (91.6) | 23 (95.8) | 273 (92.2) | |
| time = 2 | 332 (88.4) | 32 (93.9) | 364 (89.3) | |
| time = 3 | 378 (86.4) | 40 (92.1) | 418 (87.3) | |
| time = 1 | 2528 (91.6) | 484 (95.8) | 3012 (92.2) | |
| time = 2 | 2124 (88.4) | 434 (93.9) | 2558 (89.3) | |
| time = 3 | 1770 (86.4) | 384 (92.1) | 2154 (87.3) |
#5 year
summary(tableby(LDLT ~ Surv(PSURV_5year, PCENS_5year), data=CAN, times=1:5, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=3070) | 1 (N=556) | Total (N=3626) | p value | |
|---|---|---|---|---|
| Surv(PSURV_5year, PCENS_5year) | < 0.001 | |||
| time = 1 | 250 (91.6) | 23 (95.8) | 273 (92.2) | |
| time = 2 | 332 (88.4) | 32 (93.9) | 364 (89.3) | |
| time = 3 | 378 (86.4) | 40 (92.1) | 418 (87.3) | |
| time = 4 | 414 (84.5) | 44 (91.1) | 458 (85.5) | |
| time = 5 | 444 (82.6) | 48 (89.9) | 492 (83.8) | |
| time = 1 | 2528 (91.6) | 484 (95.8) | 3012 (92.2) | |
| time = 2 | 2124 (88.4) | 434 (93.9) | 2558 (89.3) | |
| time = 3 | 1770 (86.4) | 384 (92.1) | 2154 (87.3) | |
| time = 4 | 1495 (84.5) | 329 (91.1) | 1824 (85.5) | |
| time = 5 | 1234 (82.6) | 272 (89.9) | 1506 (83.8) |
#10 year
summary(tableby(LDLT ~ Surv(PSURV_10year, PCENS_10year), data=CAN, times=1:10, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=3070) | 1 (N=556) | Total (N=3626) | p value | |
|---|---|---|---|---|
| Surv(PSURV_10year, PCENS_10year) | < 0.001 | |||
| time = 1 | 250 (91.6) | 23 (95.8) | 273 (92.2) | |
| time = 2 | 332 (88.4) | 32 (93.9) | 364 (89.3) | |
| time = 3 | 378 (86.4) | 40 (92.1) | 418 (87.3) | |
| time = 4 | 414 (84.5) | 44 (91.1) | 458 (85.5) | |
| time = 5 | 444 (82.6) | 48 (89.9) | 492 (83.8) | |
| time = 6 | 462 (81.3) | 52 (88.4) | 514 (82.4) | |
| time = 7 | 486 (79.2) | 53 (87.9) | 539 (80.6) | |
| time = 8 | 498 (77.6) | 59 (84.4) | 557 (78.7) | |
| time = 9 | 504 (76.5) | 61 (82.7) | 565 (77.5) | |
| time = 10 | 508 (75.4) | 61 (82.7) | 569 (76.5) | |
| time = 1 | 2528 (91.6) | 484 (95.8) | 3012 (92.2) | |
| time = 2 | 2124 (88.4) | 434 (93.9) | 2558 (89.3) | |
| time = 3 | 1770 (86.4) | 384 (92.1) | 2154 (87.3) | |
| time = 4 | 1495 (84.5) | 329 (91.1) | 1824 (85.5) | |
| time = 5 | 1234 (82.6) | 272 (89.9) | 1506 (83.8) | |
| time = 6 | 1018 (81.3) | 225 (88.4) | 1243 (82.4) | |
| time = 7 | 763 (79.2) | 168 (87.9) | 931 (80.6) | |
| time = 8 | 536 (77.6) | 123 (84.4) | 659 (78.7) | |
| time = 9 | 350 (76.5) | 82 (82.7) | 432 (77.5) | |
| time = 10 | 178 (75.4) | 43 (82.7) | 221 (76.5) |
#Median
survfit(Surv(PSURV_years, PCENS) ~ LDLT, data = CAN)
Call: survfit(formula = Surv(PSURV_years, PCENS) ~ LDLT, data = CAN)
n events median 0.95LCL 0.95UCL
LDLT=0 3070 509 NA NA NA LDLT=1 556 61 NA NA NA
summary(survfit(Surv(PSURV_years, PCENS)~LDLT, data=CAN), times=1:10)
Call: survfit(formula = Surv(PSURV_years, PCENS) ~ LDLT, data = CAN)
LDLT=0
time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 2528 250 0.916 0.00509 0.906 0.926 2 2124 82 0.884 0.00599 0.873 0.896 3 1770 46 0.864 0.00658 0.851 0.877 4 1495 36 0.845 0.00716 0.831 0.859 5 1234 30 0.826 0.00776 0.811 0.842 6 1018 18 0.813 0.00824 0.797 0.829 7 763 24 0.792 0.00911 0.774 0.810 8 536 12 0.776 0.00996 0.757 0.796 9 350 6 0.765 0.01079 0.745 0.787 10 178 4 0.754 0.01220 0.730 0.778
LDLT=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 484 23 0.958 0.00867 0.941 0.975 2 434 9 0.939 0.01045 0.919 0.960 3 384 8 0.921 0.01204 0.898 0.945 4 329 4 0.911 0.01300 0.886 0.936 5 272 4 0.899 0.01415 0.871 0.927 6 225 4 0.884 0.01571 0.854 0.915 7 168 1 0.879 0.01646 0.847 0.912 8 123 6 0.844 0.02110 0.804 0.886 9 82 2 0.827 0.02385 0.782 0.875 10 43 0 0.827 0.02385 0.782 0.875
fit4 <- pairwise_survdiff(Surv(PSURV_years, PCENS) ~ LDLT , data = CAN)
fit4
Pairwise comparisons using Log-Rank test
data: CAN and LDLT
0
1 0.00017
P value adjustment method: BH
symnum(fit4$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
symbols = c("****", "***", "**", "*", "+", " "),
abbr.colnames = FALSE, na = "")
0
1 *** attr(,“legend”) [1] 0 ‘****’ 0.0001 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘+’ 0.1 ’ ’ 1
OS overall DDLT vs LDLT UK Figure 4c
UK <- bound3 %>% filter(COUNTRY == "UK")
fit1 <- survfit(Surv(PSURV_years, PCENS) ~ LDLT, data = UK)
ggsurv1 <- ggsurvplot(
fit1,
data = UK,
risk.table = TRUE,
pval = TRUE,
pval.coord = c(0, 0.8),
pval.size = 6,
conf.int = F,
xlim = c(0,5),
ylim = c(0.5, 1.00),
xlab = "Years from LT",
palette = "jama",
break.time.by = 1,
title = "United Kingdom",
# subtitle = "with 95% confidence intervals",
ggtheme = theme_test(base_size=28, base_family = "Helvetica"),
# risk.table.y.text.col = F,
fontsize=8,
risk.table.height = 0.25,
# risk.table.y.text = T,
# surv.median.line = "hv",
legend.title= "",
tables.x.text = FALSE,
legend.labs =
c("DDLT", "LDLT")
)
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
ggsurv1
## Warning: Removed 5 row(s) containing missing values (geom_path).
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 5 row(s) containing missing values (geom_path).
## Warning: Removed 4 rows containing missing values (geom_point).
ggsurv1$plot <- ggsurv1$plot+
ggplot2::annotate("text",
x = 2.5, y = 0.5, # x and y coordinates of the text
label = "Adjusted HR (ref: DDLT, HR LDLT 1.20, 95% CI 0.64-2.26)", size = 6)
ggsurv1
## Warning: Removed 5 row(s) containing missing values (geom_path).
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 5 row(s) containing missing values (geom_path).
## Warning: Removed 4 rows containing missing values (geom_point).
#Overall
summary(tableby(LDLT ~ Surv(PSURV_years, PCENS), data=UK, times=1:10, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=6498) | 1 (N=97) | Total (N=6595) | p value | |
|---|---|---|---|---|
| Surv(PSURV_years, PCENS) | 0.686 | |||
| time = 1 | 433 (93.2) | 8 (91.4) | 441 (93.2) | |
| time = 2 | 579 (90.6) | 9 (90.0) | 588 (90.6) | |
| time = 3 | 717 (87.7) | 9 (90.0) | 726 (87.7) | |
| time = 4 | 834 (84.7) | 10 (88.0) | 844 (84.8) | |
| time = 5 | 917 (82.2) | 11 (85.4) | 928 (82.3) | |
| time = 6 | 986 (79.6) | 11 (85.4) | 997 (79.6) | |
| time = 7 | 1034 (77.2) | 12 (80.0) | 1046 (77.3) | |
| time = 8 | 1074 (74.6) | 12 (80.0) | 1086 (74.7) | |
| time = 9 | 1098 (72.4) | 13 (66.7) | 1111 (72.3) | |
| time = 10 | 1117 (69.4) | 13 (66.7) | 1130 (69.4) | |
| time = 1 | 5531 (93.2) | 79 (91.4) | 5610 (93.2) | |
| time = 2 | 4575 (90.6) | 65 (90.0) | 4640 (90.6) | |
| time = 3 | 3750 (87.7) | 50 (90.0) | 3800 (87.7) | |
| time = 4 | 3059 (84.7) | 37 (88.0) | 3096 (84.8) | |
| time = 5 | 2376 (82.2) | 27 (85.4) | 2403 (82.3) | |
| time = 6 | 1796 (79.6) | 19 (85.4) | 1815 (79.6) | |
| time = 7 | 1319 (77.2) | 12 (80.0) | 1331 (77.3) | |
| time = 8 | 945 (74.6) | 6 (80.0) | 951 (74.7) | |
| time = 9 | 618 (72.4) | 4 (66.7) | 622 (72.3) | |
| time = 10 | 326 (69.4) | 2 (66.7) | 328 (69.4) |
#1 year
summary(tableby(LDLT ~ Surv(PSURV_1year, PCENS_1year), data=UK, times=1:1, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=6498) | 1 (N=97) | Total (N=6595) | p value | |
|---|---|---|---|---|
| Surv(PSURV_1year, PCENS_1year) | 0.521 | |||
| time = 1 | 433 (93.2) | 8 (91.4) | 441 (93.2) | |
| time = 1 | 5531 (93.2) | 79 (91.4) | 5610 (93.2) |
#3 year
summary(tableby(LDLT ~ Surv(PSURV_3year, PCENS_3year), data=UK, times=1:3, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=6498) | 1 (N=97) | Total (N=6595) | p value | |
|---|---|---|---|---|
| Surv(PSURV_3year, PCENS_3year) | 0.692 | |||
| time = 1 | 433 (93.2) | 8 (91.4) | 441 (93.2) | |
| time = 2 | 579 (90.6) | 9 (90.0) | 588 (90.6) | |
| time = 3 | 717 (87.7) | 9 (90.0) | 726 (87.7) | |
| time = 1 | 5531 (93.2) | 79 (91.4) | 5610 (93.2) | |
| time = 2 | 4575 (90.6) | 65 (90.0) | 4640 (90.6) | |
| time = 3 | 3750 (87.7) | 50 (90.0) | 3800 (87.7) |
#5 year
summary(tableby(LDLT ~ Surv(PSURV_5year, PCENS_5year), data=UK, times=1:5, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=6498) | 1 (N=97) | Total (N=6595) | p value | |
|---|---|---|---|---|
| Surv(PSURV_5year, PCENS_5year) | 0.626 | |||
| time = 1 | 433 (93.2) | 8 (91.4) | 441 (93.2) | |
| time = 2 | 579 (90.6) | 9 (90.0) | 588 (90.6) | |
| time = 3 | 717 (87.7) | 9 (90.0) | 726 (87.7) | |
| time = 4 | 834 (84.7) | 10 (88.0) | 844 (84.8) | |
| time = 5 | 917 (82.2) | 11 (85.4) | 928 (82.3) | |
| time = 1 | 5531 (93.2) | 79 (91.4) | 5610 (93.2) | |
| time = 2 | 4575 (90.6) | 65 (90.0) | 4640 (90.6) | |
| time = 3 | 3750 (87.7) | 50 (90.0) | 3800 (87.7) | |
| time = 4 | 3059 (84.7) | 37 (88.0) | 3096 (84.8) | |
| time = 5 | 2376 (82.2) | 27 (85.4) | 2403 (82.3) |
#10 year
summary(tableby(LDLT ~ Surv(PSURV_10year, PCENS_10year), data=UK, times=1:10, surv.stats=c("NeventsSurv", "NriskSurv")))
| 0 (N=6498) | 1 (N=97) | Total (N=6595) | p value | |
|---|---|---|---|---|
| Surv(PSURV_10year, PCENS_10year) | 0.691 | |||
| time = 1 | 433 (93.2) | 8 (91.4) | 441 (93.2) | |
| time = 2 | 579 (90.6) | 9 (90.0) | 588 (90.6) | |
| time = 3 | 717 (87.7) | 9 (90.0) | 726 (87.7) | |
| time = 4 | 834 (84.7) | 10 (88.0) | 844 (84.8) | |
| time = 5 | 917 (82.2) | 11 (85.4) | 928 (82.3) | |
| time = 6 | 986 (79.6) | 11 (85.4) | 997 (79.6) | |
| time = 7 | 1034 (77.2) | 12 (80.0) | 1046 (77.3) | |
| time = 8 | 1074 (74.6) | 12 (80.0) | 1086 (74.7) | |
| time = 9 | 1098 (72.4) | 13 (66.7) | 1111 (72.3) | |
| time = 10 | 1117 (69.4) | 13 (66.7) | 1130 (69.4) | |
| time = 1 | 5531 (93.2) | 79 (91.4) | 5610 (93.2) | |
| time = 2 | 4575 (90.6) | 65 (90.0) | 4640 (90.6) | |
| time = 3 | 3750 (87.7) | 50 (90.0) | 3800 (87.7) | |
| time = 4 | 3059 (84.7) | 37 (88.0) | 3096 (84.8) | |
| time = 5 | 2376 (82.2) | 27 (85.4) | 2403 (82.3) | |
| time = 6 | 1796 (79.6) | 19 (85.4) | 1815 (79.6) | |
| time = 7 | 1319 (77.2) | 12 (80.0) | 1331 (77.3) | |
| time = 8 | 945 (74.6) | 6 (80.0) | 951 (74.7) | |
| time = 9 | 618 (72.4) | 4 (66.7) | 622 (72.3) | |
| time = 10 | 326 (69.4) | 2 (66.7) | 328 (69.4) |
#Median
survfit(Surv(PSURV_years, PCENS) ~ LDLT, data = UK)
Call: survfit(formula = Surv(PSURV_years, PCENS) ~ LDLT, data = UK)
n events median 0.95LCL 0.95UCL
LDLT=0 6498 1132 11.9 11.93 NA LDLT=1 97 13 NA 8.13 NA
summary(survfit(Surv(PSURV_years, PCENS)~LDLT, data=UK), times=1:10)
Call: survfit(formula = Surv(PSURV_years, PCENS) ~ LDLT, data = UK)
LDLT=0
time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 5531 433 0.932 0.00315 0.926 0.938 2 4575 146 0.906 0.00374 0.899 0.913 3 3750 138 0.877 0.00436 0.868 0.885 4 3059 117 0.847 0.00499 0.838 0.857 5 2376 83 0.822 0.00556 0.811 0.833 6 1796 69 0.796 0.00624 0.783 0.808 7 1319 48 0.772 0.00692 0.759 0.786 8 945 40 0.746 0.00783 0.731 0.761 9 618 24 0.724 0.00885 0.706 0.741 10 326 19 0.694 0.01082 0.673 0.716
LDLT=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 79 8 0.914 0.0293 0.858 0.973 2 65 1 0.900 0.0317 0.840 0.965 3 50 0 0.900 0.0317 0.840 0.965 4 37 1 0.880 0.0368 0.811 0.955 5 27 1 0.854 0.0443 0.771 0.945 6 19 0 0.854 0.0443 0.771 0.945 7 12 1 0.800 0.0663 0.680 0.941 8 6 0 0.800 0.0663 0.680 0.941 9 4 1 0.667 0.1337 0.450 0.988 10 2 0 0.667 0.1337 0.450 0.988
fit4 <- pairwise_survdiff(Surv(PSURV_years, PCENS) ~ LDLT , data = UK)
fit4
Pairwise comparisons using Log-Rank test
data: UK and LDLT
0
1 0.69
P value adjustment method: BH
symnum(fit4$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
symbols = c("****", "***", "**", "*", "+", " "),
abbr.colnames = FALSE, na = "")
0 1
attr(,“legend”) [1] 0 ‘****’ 0.0001 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘+’ 0.1 ’ ’ 1
OS overall HCC Figure S2a
HCConly <- bound2 %>% filter(HCC == "1")
fit1 <- survfit(Surv(PSURV_years, PCENS) ~ COUNTRY, data = HCConly)
ggsurv1 <- ggsurvplot(
fit1,
data = HCConly,
risk.table = TRUE,
pval = TRUE,
pval.coord = c(0, 0.8),
pval.size = 6,
conf.int = F,
xlim = c(0,5),
ylim = c(0.7, 1.00),
xlab = "Years from LT",
palette = "jama", #dont use red and green
break.time.by = 1,
title = "",
# subtitle = "with 95% confidence intervals",
ggtheme = theme_test(base_size=28, base_family = "Helvetica"),
# risk.table.y.text.col = F,
fontsize=8,
risk.table.height = 0.25,
# risk.table.y.text = T,
# surv.median.line = "hv",
legend.title= "",
tables.x.text = FALSE,
legend.labs =
c("US", "Canada", "UK")
)
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
ggsurv1$plot <- ggsurv1$plot+
ggplot2::annotate("text",
x = 2.5, y = 0.7, # x and y coordinates of the text
label = "Adjusted HR (ref: US, Canada HR 0.20, 95% CI 0.02-1.66, UK HR 0.38, 95% CI 0.05-2.96)", size = 6)
ggsurv1
## Warning: Removed 84 row(s) containing missing values (geom_path).
## Warning: Removed 71 rows containing missing values (geom_point).
## Warning: Removed 84 row(s) containing missing values (geom_path).
## Warning: Removed 71 rows containing missing values (geom_point).
OS overall non-HCC Figure S2b
nonHCC <- bound2 %>% filter(HCC == "0")
fit1 <- survfit(Surv(PSURV_years, PCENS) ~ COUNTRY, data = nonHCC)
ggsurv1 <- ggsurvplot(
fit1,
data = nonHCC,
risk.table = TRUE,
pval = TRUE,
pval.coord = c(0, 0.8),
pval.size = 6,
conf.int = F,
xlim = c(0,5),
ylim = c(0.7, 1.00),
xlab = "Years from LT",
palette = "jama",
break.time.by = 1,
title = "",
# subtitle = "with 95% confidence intervals",
ggtheme = theme_test(base_size=28, base_family = "Helvetica"),
# risk.table.y.text.col = F,
fontsize=8,
risk.table.height = 0.25,
# risk.table.y.text = T,
# surv.median.line = "hv",
legend.title= "",
tables.x.text = FALSE,
legend.labs =
c("US", "Canada", "UK")
)
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
ggsurv1
## Warning: Removed 15 row(s) containing missing values (geom_path).
## Warning: Removed 14 rows containing missing values (geom_point).
## Warning: Removed 15 row(s) containing missing values (geom_path).
## Warning: Removed 14 rows containing missing values (geom_point).
ggsurv1$plot <- ggsurv1$plot+
ggplot2::annotate("text",
x = 2.5, y = 0.7, # x and y coordinates of the text
label = "Adjusted HR (ref: US, Canada HR 0.51, 95% CI 0.22-1.18, UK HR 1.36, 95% CI 0.69-2.67)", size = 6)
ggsurv1
## Warning: Removed 15 row(s) containing missing values (geom_path).
## Warning: Removed 14 rows containing missing values (geom_point).
## Warning: Removed 15 row(s) containing missing values (geom_path).
## Warning: Removed 14 rows containing missing values (geom_point).
confint.coxme <- function(object, parm=NULL, level=0.95, ..., more=FALSE){
if(!is.null(parm)) warning("[confint.coxme] argument 'parm' doesn't do anything for this method")
if(level != 0.95) warning("[confint.coxme] 'level' will be 0.95 regardless of what argument you give it. Ha!")
co <- object$coef
se <- sqrt(diag(stats::vcov(object)))
m <- matrix(c(co - 2*se, co + 2*se), ncol=2)
colnames(m) <- c("2.5 %", "97.5 %")
rownames(m) <- names(co)
if(more){
p <- 2*stats::pnorm(abs(co/se), lower.tail=F)
m <- cbind(m, co, p)
rownames(m)[3:4] <- c("coef", "p")
}
return (m)
}
US vs. UK vs. CAN Forest model patient survival
bound4 <- bound2 %>% filter(COUNTRY == "US" | COUNTRY == "UK" | COUNTRY == "CAN")
#Uni
cox1 <- coxph(Surv(PSURV,PCENS)~ COUNTRY, data=bound4)
forest_model(cox1)
#Just recipient
cox1 <- coxme(formula= Surv(PSURV_years,PCENS)~COUNTRY+
#RSEX+
BMI+
RBILIRUBIN+
RINR+
RCREAT+
RREN_SUP+
RAGE+
WAITLIST_TIME+
TX_YR+
HCV+
HCC+
NASH+
ALD+
(1|TRANSPLANT_UNIT),
data=bound4)
cox1
Cox mixed-effects model fit by maximum likelihood Data: bound4 events, n = 414, 2692 (394 observations deleted due to missingness) Iterations= 11 80 NULL Integrated Fitted Log-likelihood -3040.108 -3003.128 -2996.95
Chisq df p AIC BIC
Integrated loglik 73.96 15.00 0.00000000087150 43.96 -16.43 Penalized loglik 86.32 19.44 0.00000000020941 47.44 -30.81
Model: Surv(PSURV_years, PCENS) ~ COUNTRY + BMI + RBILIRUBIN + RINR + RCREAT + RREN_SUP + RAGE + WAITLIST_TIME + TX_YR + HCV + HCC + NASH + ALD + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p COUNTRYCAN -0.5311240589 0.5879437 0.2375154734 -2.24 0.025000 COUNTRYUK 0.2855994130 1.3305593 0.3080447373 0.93 0.350000 BMI -0.0087710805 0.9912673 0.0103955919 -0.84 0.400000 RBILIRUBIN -0.0090471908 0.9909936 0.0122679672 -0.74 0.460000 RINR -0.0476529127 0.9534647 0.0963508969 -0.49 0.620000 RCREAT 0.2525526779 1.2873073 0.0741830872 3.40 0.000660 RREN_SUPPre-tx support 0.8457528619 2.3297311 0.4370902360 1.93 0.053000 RAGE 0.0189438183 1.0191244 0.0048279739 3.92 0.000087 WAITLIST_TIME -0.0000445156 0.9999555 0.0000972877 -0.46 0.650000 TX_YR -0.0348418882 0.9657581 0.0191004486 -1.82 0.068000 HCV1 -0.6577760333 0.5180021 0.3290465504 -2.00 0.046000 HCC1 0.3612829760 1.4351695 0.1244709393 2.90 0.003700 NASH1 -0.0460932088 0.9549529 0.1574176358 -0.29 0.770000 ALD1 -0.1821803103 0.8334510 0.1841702976 -0.99 0.320000
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.13802843 0.01905185
confint.coxme(cox1)
2.5 % 97.5 %
COUNTRYCAN -1.006155006 -0.0560931122 COUNTRYUK -0.330490062 0.9016888875 BMI -0.029562264 0.0120201032 RBILIRUBIN -0.033583125 0.0154887436 RINR -0.240354707 0.1450488811 RCREAT 0.104186503 0.4009188524 RREN_SUPPre-tx support -0.028427610 1.7199333340 RAGE 0.009287871 0.0285997661 WAITLIST_TIME -0.000239091 0.0001500598 TX_YR -0.073042785 0.0033590090 HCV1 -1.315869134 0.0003170674 HCC1 0.112341097 0.6102248546 NASH1 -0.360928480 0.2687420627 ALD1 -0.550520905 0.1861602848
#Just donor
cox1 <- coxme(formula= Surv(PSURV_years,PCENS)~COUNTRY+
CIT+
DAGE+
DSEX+
TX_YR+
(1|TRANSPLANT_UNIT),
data=bound4)
cox1
Cox mixed-effects model fit by maximum likelihood Data: bound4 events, n = 389, 2756 (330 observations deleted due to missingness) Iterations= 11 58 NULL Integrated Fitted Log-likelihood -2856.467 -2836.57 -2832.481
Chisq df p AIC BIC
Integrated loglik 39.79 7.0 0.00000137840 25.79 -1.95 Penalized loglik 47.97 9.7 0.00000048396 28.57 -9.87
Model: Surv(PSURV_years, PCENS) ~ COUNTRY + CIT + DAGE + DSEX + TX_YR + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p COUNTRYCAN -0.81837773001 0.4411467 0.2241123247 -3.65 0.000260 COUNTRYUK -0.00073967936 0.9992606 0.3006234626 0.00 1.000000 CIT 0.00008032782 1.0000803 0.0002061973 0.39 0.700000 DAGE 0.01993839259 1.0201385 0.0048941671 4.07 0.000046 DSEXMale 0.08047482060 1.0838016 0.1031549993 0.78 0.440000 TX_YR -0.02327597647 0.9769928 0.0191499160 -1.22 0.220000
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.11222883 0.01259531
confint.coxme(cox1)
2.5 % 97.5 %
COUNTRYCAN -1.2666023795 -0.3701530805 COUNTRYUK -0.6019866045 0.6005072458 CIT -0.0003320668 0.0004927224 DAGE 0.0101500583 0.0297267268 DSEXMale -0.1258351780 0.2867848192 TX_YR -0.0615758085 0.0150238556
cox2 <- coxme(formula= Surv(PSURV_years,PCENS)~COUNTRY+
#RSEX+
BMI+
RBILIRUBIN+
RINR+
RCREAT+
RREN_SUP+
RAGE+
WAITLIST_TIME+
TX_YR+
HCV+
HCC+
NASH+
ALD+
CIT+
DAGE+
DSEX+
(1|TRANSPLANT_UNIT),
data=bound4)
cox2
Cox mixed-effects model fit by maximum likelihood Data: bound4 events, n = 370, 2454 (632 observations deleted due to missingness) Iterations= 15 108 NULL Integrated Fitted Log-likelihood -2672.585 -2630.344 -2625.967
Chisq df p AIC BIC
Integrated loglik 84.48 18.00 0.000000000139260 48.48 -21.96 Penalized loglik 93.24 20.94 0.000000000042409 51.35 -30.60
Model: Surv(PSURV_years, PCENS) ~ COUNTRY + BMI + RBILIRUBIN + RINR + RCREAT + RREN_SUP + RAGE + WAITLIST_TIME + TX_YR + HCV + HCC + NASH + ALD + CIT + DAGE + DSEX + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p COUNTRYCAN -0.63572243800 0.5295528 0.38285523502 -1.66 0.097000 COUNTRYUK 0.29150188194 1.3384362 0.31812715216 0.92 0.360000 BMI -0.03378677979 0.9667776 0.01174515328 -2.88 0.004000 RBILIRUBIN -0.00500563594 0.9950069 0.01298705057 -0.39 0.700000 RINR -0.04096760903 0.9598602 0.09891411935 -0.41 0.680000 RCREAT 0.28150105342 1.3251174 0.08429903733 3.34 0.000840 RREN_SUPPre-tx support 0.60753251684 1.8358958 0.47870798106 1.27 0.200000 RAGE 0.02163205949 1.0218677 0.00504329523 4.29 0.000018 WAITLIST_TIME -0.00003437378 0.9999656 0.00009670316 -0.36 0.720000 TX_YR -0.03111807712 0.9693611 0.02021414227 -1.54 0.120000 HCV1 -1.18034770023 0.3071719 0.51036105217 -2.31 0.021000 HCC1 0.30786328749 1.3605150 0.13355522904 2.31 0.021000 NASH1 0.04476354781 1.0457806 0.16283139068 0.27 0.780000 ALD1 -0.07171426584 0.9307968 0.18612856088 -0.39 0.700000 CIT 0.00006667081 1.0000667 0.00020777368 0.32 0.750000 DAGE 0.01666608413 1.0168057 0.00514656037 3.24 0.001200 DSEXMale 0.09010499534 1.0942892 0.10635731764 0.85 0.400000
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.12042989 0.01450336
confint.coxme(cox2)
2.5 % 97.5 %
COUNTRYCAN -1.4014329080 0.1299880320 COUNTRYUK -0.3447524224 0.9277561863 BMI -0.0572770864 -0.0102964732 RBILIRUBIN -0.0309797371 0.0209684652 RINR -0.2387958477 0.1568606297 RCREAT 0.1129029788 0.4500991281 RREN_SUPPre-tx support -0.3498834453 1.5649484790 RAGE 0.0115454690 0.0317186499 WAITLIST_TIME -0.0002277801 0.0001590325 TX_YR -0.0715463617 0.0093102074 HCV1 -2.2010698046 -0.1596255959 HCC1 0.0407528294 0.5749737456 NASH1 -0.2808992336 0.3704263292 ALD1 -0.4439713876 0.3005428559 CIT -0.0003488765 0.0004822182 DAGE 0.0063729634 0.0269592049 DSEXMale -0.1226096399 0.3028196306
US vs. UK vs. CAN Forest model graft survival
bound4 <- bound2 %>% filter(COUNTRY == "US" | COUNTRY == "UK" | COUNTRY == "CAN")
#Uni
cox1 <- coxph(Surv(GSURV_years, GCENS)~ COUNTRY, data=bound4)
forest_model(cox1)
#Just recipient
cox1 <- coxme(formula= Surv(GSURV_years, GCENS)~COUNTRY+
#RSEX+
BMI+
RBILIRUBIN+
RINR+
RCREAT+
RREN_SUP+
RAGE+
WAITLIST_TIME+
TX_YR+
HCV+
HCC+
NASH+
ALD+
(1|TRANSPLANT_UNIT),
data=bound4)
cox1
Cox mixed-effects model fit by maximum likelihood Data: bound4 events, n = 578, 2692 (394 observations deleted due to missingness) Iterations= 5 33 NULL Integrated Fitted Log-likelihood -4293.493 -4266.617 -4260.902
Chisq df p AIC BIC
Integrated loglik 53.75 15.00 0.00000288840 23.75 -41.64 Penalized loglik 65.18 19.11 0.00000061248 26.97 -56.34
Model: Surv(GSURV_years, GCENS) ~ COUNTRY + BMI + RBILIRUBIN + RINR + RCREAT + RREN_SUP + RAGE + WAITLIST_TIME + TX_YR + HCV + HCC + NASH + ALD + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p COUNTRYCAN -0.610558564149 0.5430475 0.2094216039 -2.92 0.00360 COUNTRYUK -0.126940611306 0.8807860 0.3015188443 -0.42 0.67000 BMI -0.033234897047 0.9673113 0.0094756215 -3.51 0.00045 RBILIRUBIN -0.000651288247 0.9993489 0.0088870342 -0.07 0.94000 RINR 0.007262250223 1.0072887 0.0630546708 0.12 0.91000 RCREAT 0.219535233446 1.2454977 0.0716568453 3.06 0.00220 RREN_SUPPre-tx support 0.273768216684 1.3149100 0.4460513851 0.61 0.54000 RAGE -0.000176919462 0.9998231 0.0036667537 -0.05 0.96000 WAITLIST_TIME 0.000001032512 1.0000010 0.0000761251 0.01 0.99000 TX_YR -0.030819085407 0.9696510 0.0154761589 -1.99 0.04600 HCV1 -0.819829727009 0.4405067 0.3249926519 -2.52 0.01200 HCC1 0.280171824527 1.3233572 0.1116098632 2.51 0.01200 NASH1 0.098851075646 1.1039019 0.1365813115 0.72 0.47000 ALD1 -0.073951936862 0.9287163 0.1514441783 -0.49 0.63000
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.11106823 0.01233615
confint.coxme(cox1)
2.5 % 97.5 %
COUNTRYCAN -1.0294017720 -0.1917153563 COUNTRYUK -0.7299782999 0.4760970773 BMI -0.0521861401 -0.0142836540 RBILIRUBIN -0.0184253567 0.0171227802 RINR -0.1188470915 0.1333715919 RCREAT 0.0762215429 0.3628489240 RREN_SUPPre-tx support -0.6183345536 1.1658709869 RAGE -0.0075104268 0.0071565878 WAITLIST_TIME -0.0001512177 0.0001532827 TX_YR -0.0617714031 0.0001332323 HCV1 -1.4698150307 -0.1698444233 HCC1 0.0569520982 0.5033915509 NASH1 -0.1743115474 0.3720136987 ALD1 -0.3768402934 0.2289364197
#Just donor
cox1 <- coxme(formula= Surv(GSURV_years, GCENS)~COUNTRY+
CIT+
DAGE+
DSEX+
TX_YR+
(1|TRANSPLANT_UNIT),
data=bound4)
cox1
Cox mixed-effects model fit by maximum likelihood Data: bound4 events, n = 550, 2756 (330 observations deleted due to missingness) Iterations= 5 27 NULL Integrated Fitted Log-likelihood -4090.247 -4063.92 -4060.432
Chisq df p AIC BIC
Integrated loglik 52.65 7.00 0.0000000043389 38.65 8.48 Penalized loglik 59.63 9.18 0.0000000018934 41.27 1.72
Model: Surv(GSURV_years, GCENS) ~ COUNTRY + CIT + DAGE + DSEX + TX_YR + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p COUNTRYCAN -0.72102756584 0.4862523 0.1830105943 -3.94 0.0000820 COUNTRYUK -0.29139845920 0.7472179 0.2966431756 -0.98 0.3300000 CIT -0.00002391839 0.9999761 0.0001965816 -0.12 0.9000000 DAGE 0.02006867245 1.0202714 0.0041112230 4.88 0.0000011 DSEXMale 0.00791046142 1.0079418 0.0867379149 0.09 0.9300000 TX_YR -0.03139422217 0.9690935 0.0154944159 -2.03 0.0430000
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.086267299 0.007442047
confint.coxme(cox1)
2.5 % 97.5 %
COUNTRYCAN -1.0870487544 -0.3550063773 COUNTRYUK -0.8846848104 0.3018878919 CIT -0.0004170816 0.0003692448 DAGE 0.0118462265 0.0282911184 DSEXMale -0.1655653683 0.1813862912 TX_YR -0.0623830540 -0.0004053904
cox2 <- coxme(formula= Surv(GSURV_years, GCENS)~COUNTRY+
#RSEX+
BMI+
RBILIRUBIN+
RINR+
RCREAT+
RREN_SUP+
RAGE+
WAITLIST_TIME+
TX_YR+
HCV+
HCC+
NASH+
ALD+
CIT+
DAGE+
DSEX+
(1|TRANSPLANT_UNIT),
data=bound4)
cox2
Cox mixed-effects model fit by maximum likelihood Data: bound4 events, n = 522, 2454 (632 observations deleted due to missingness) Iterations= 5 32 NULL Integrated Fitted Log-likelihood -3824.806 -3791.884 -3788.624
Chisq df p AIC BIC
Integrated loglik 65.84 18.00 0.000000225150 29.84 -46.79 Penalized loglik 72.36 19.99 0.000000073919 32.39 -52.71
Model: Surv(GSURV_years, GCENS) ~ COUNTRY + BMI + RBILIRUBIN + RINR + RCREAT + RREN_SUP + RAGE + WAITLIST_TIME + TX_YR + HCV + HCC + NASH + ALD + CIT + DAGE + DSEX + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p COUNTRYCAN -0.607031225699 0.5449664 0.30955812759 -1.96 0.05000 COUNTRYUK -0.096185415670 0.9082956 0.31216577522 -0.31 0.76000 BMI -0.035293132541 0.9653224 0.00995385102 -3.55 0.00039 RBILIRUBIN 0.005831617010 1.0058487 0.00923977530 0.63 0.53000 RINR -0.000788312547 0.9992120 0.06828058260 -0.01 0.99000 RCREAT 0.235429885844 1.2654527 0.08122336231 2.90 0.00370 RREN_SUPPre-tx support 0.008258619433 1.0082928 0.50200452296 0.02 0.99000 RAGE 0.002632655953 1.0026361 0.00388534503 0.68 0.50000 WAITLIST_TIME -0.000007797856 0.9999922 0.00007801948 -0.10 0.92000 TX_YR -0.036091403477 0.9645521 0.01619140184 -2.23 0.02600 HCV1 -1.161818804516 0.3129165 0.45436992157 -2.56 0.01100 HCC1 0.258328576372 1.2947642 0.11740904739 2.20 0.02800 NASH1 0.104915180452 1.1106164 0.14075116123 0.75 0.46000 ALD1 -0.038070621089 0.9626450 0.15562983286 -0.24 0.81000 CIT -0.000007497090 0.9999925 0.00019703100 -0.04 0.97000 DAGE 0.018274758472 1.0184428 0.00427990577 4.27 0.00002 DSEXMale 0.019382431009 1.0195715 0.08933865860 0.22 0.83000
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.085642010 0.007334554
confint.coxme(cox2)
2.5 % 97.5 %
COUNTRYCAN -1.2261474809 0.0120850295 COUNTRYUK -0.7205169661 0.5281461348 BMI -0.0552008346 -0.0153854305 RBILIRUBIN -0.0126479336 0.0243111676 RINR -0.1373494777 0.1357728526 RCREAT 0.0729831612 0.3978766105 RREN_SUPPre-tx support -0.9957504265 1.0122676654 RAGE -0.0051380341 0.0104033460 WAITLIST_TIME -0.0001638368 0.0001482411 TX_YR -0.0684742072 -0.0037085998 HCV1 -2.0705586476 -0.2530789614 HCC1 0.0235104816 0.4931466712 NASH1 -0.1765871420 0.3864175029 ALD1 -0.3493302868 0.2731890446 CIT -0.0004015591 0.0003865649 DAGE 0.0097149469 0.0268345700 DSEXMale -0.1592948862 0.1980597482
#Uni
cox1 <- coxph(Surv(PSURV,PCENS)~ LDLT, data=US)
forest_model(cox1)
## Resized limits to included dashed line in forest panel
#Just recipient
cox1 <- coxme(formula= Surv(PSURV_years,PCENS)~LDLT+
# RSEX+
BMI+
RBILIRUBIN+
RINR+
RCREAT+
RREN_SUP+
RAGE+
WAITLIST_TIME+
TX_YR+
HCV+
HCC+
NASH+
ALD+
(1|TRANSPLANT_UNIT),
data=US)
cox1
Cox mixed-effects model fit by maximum likelihood Data: US events, n = 13054, 60055 (130 observations deleted due to missingness) Iterations= 18 94 NULL Integrated Fitted Log-likelihood -136255.1 -135580.3 -135461.9
Chisq df p AIC BIC
Integrated loglik 1349.68 14.00 0 1321.68 1217.00 Penalized loglik 1586.40 93.65 0 1399.10 698.91
Model: Surv(PSURV_years, PCENS) ~ LDLT + BMI + RBILIRUBIN + RINR + RCREAT + RREN_SUP + RAGE + WAITLIST_TIME + TX_YR + HCV + HCC + NASH + ALD + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p LDLT1 -0.138396072553 0.8707537 0.05482507482 -2.52 0.01200 BMI -0.005333791129 0.9946804 0.00162002063 -3.29 0.00099 RBILIRUBIN 0.002358194938 1.0023610 0.00093278365 2.53 0.01100 RINR -0.002658872265 0.9973447 0.00741570882 -0.36 0.72000 RCREAT 0.079546683215 1.0827961 0.00808780178 9.84 0.00000 RREN_SUPPre-tx support 0.344891007973 1.4118360 0.03230276716 10.68 0.00000 RAGE 0.020150945955 1.0203553 0.00103200067 19.53 0.00000 WAITLIST_TIME 0.000006135301 1.0000061 0.00001796821 0.34 0.73000 TX_YR -0.049904919977 0.9513199 0.00337978386 -14.77 0.00000 HCV1 -0.001301056103 0.9986998 0.03952833720 -0.03 0.97000 HCC1 0.201305676124 1.2229986 0.02155153884 9.34 0.00000 NASH1 -0.027697407178 0.9726826 0.02787684515 -0.99 0.32000 ALD1 -0.080163957682 0.9229650 0.02908264164 -2.76 0.00580
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.17625600 0.03106618
confint.coxme(cox1)
2.5 % 97.5 %
LDLT1 -0.24804622219 -0.02874592292 BMI -0.00857383239 -0.00209374987 RBILIRUBIN 0.00049262763 0.00422376224 RINR -0.01749028990 0.01217254537 RCREAT 0.06337107966 0.09572228677 RREN_SUPPre-tx support 0.28028547365 0.40949654230 RAGE 0.01808694461 0.02221494730 WAITLIST_TIME -0.00002980112 0.00004207172 TX_YR -0.05666448769 -0.04314535226 HCV1 -0.08035773050 0.07775561829 HCC1 0.15820259845 0.24440875380 NASH1 -0.08345109748 0.02805628312 ALD1 -0.13832924095 -0.02199867441
#Just donor
cox1 <- coxme(formula= Surv(PSURV_years,PCENS)~LDLT+
CIT+
DAGE+
DSEX+
TX_YR+
(1|TRANSPLANT_UNIT),
data=US)
cox1
Cox mixed-effects model fit by maximum likelihood Data: US events, n = 12924, 59695 (490 observations deleted due to missingness) Iterations= 17 72 NULL Integrated Fitted Log-likelihood -134809.3 -134480.3 -134365.8
Chisq df p AIC BIC
Integrated loglik 658.02 6.00 0 646.02 601.22 Penalized loglik 887.02 84.18 0 718.67 90.14
Model: Surv(PSURV_years, PCENS) ~ LDLT + CIT + DAGE + DSEX + TX_YR + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p LDLT1 -0.1428864758 0.8668525 0.05746830508 -2.49 0.0130000000 CIT 0.0002993341 1.0002994 0.00005066372 5.91 0.0000000035 DAGE 0.0079183288 1.0079498 0.00054805557 14.45 0.0000000000 DSEXMale 0.0333538423 1.0339163 0.01817504103 1.84 0.0660000000 TX_YR -0.0408393881 0.9599833 0.00334509653 -12.21 0.0000000000
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.17015352 0.02895222
confint.coxme(cox1)
2.5 % 97.5 %
LDLT1 -0.2578230860 -0.0279498657 CIT 0.0001980067 0.0004006615 DAGE 0.0068222176 0.0090144399 DSEXMale -0.0029962398 0.0697039243 TX_YR -0.0475295812 -0.0341491951
#donor and recipient
cox2 <- coxme(formula= Surv(PSURV_years,PCENS)~LDLT+
#RSEX+
BMI+
RBILIRUBIN+
RINR+
RCREAT+
RREN_SUP+
RAGE+
WAITLIST_TIME+
TX_YR+
HCV+
HCC+
NASH+
ALD+
CIT+
DAGE+
DSEX+
(1|TRANSPLANT_UNIT),
data=US)
cox2
Cox mixed-effects model fit by maximum likelihood Data: US events, n = 12893, 59567 (618 observations deleted due to missingness) Iterations= 19 99 NULL Integrated Fitted Log-likelihood -134463.3 -133688.9 -133571.1
Chisq df p AIC BIC
Integrated loglik 1548.76 17.00 0 1514.76 1387.86 Penalized loglik 1784.42 96.36 0 1591.70 872.40
Model: Surv(PSURV_years, PCENS) ~ LDLT + BMI + RBILIRUBIN + RINR + RCREAT + RREN_SUP + RAGE + WAITLIST_TIME + TX_YR + HCV + HCC + NASH + ALD + CIT + DAGE + DSEX + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z LDLT1 0.0310905119085 1.0315789 0.05834312860 0.53 BMI -0.0060951129791 0.9939234 0.00163570893 -3.73 RBILIRUBIN 0.0029983182905 1.0030028 0.00093552912 3.20 RINR -0.0000705908230 0.9999294 0.00723002548 -0.01 RCREAT 0.0805673550288 1.0839019 0.00809120861 9.96 RREN_SUPPre-tx support 0.3654363945530 1.4411428 0.03249890432 11.24 RAGE 0.0192502364733 1.0194367 0.00104292529 18.46 WAITLIST_TIME 0.0000007959007 1.0000008 0.00001809947 0.04 TX_YR -0.0471220675239 0.9539709 0.00342447487 -13.76 HCV1 -0.0035821793134 0.9964242 0.03976106539 -0.09 HCC1 0.2069491588403 1.2299200 0.02167981000 9.55 NASH1 -0.0405861375053 0.9602264 0.02804873689 -1.45 ALD1 -0.0960670891460 0.9084031 0.02925449134 -3.28 CIT 0.0003296642817 1.0003297 0.00005011103 6.58 DAGE 0.0076736704493 1.0077032 0.00055385747 13.85 DSEXMale 0.0372574774604 1.0379602 0.01823068689 2.04 p LDLT1 0.590000000000 BMI 0.000190000000 RBILIRUBIN 0.001400000000 RINR 0.990000000000 RCREAT 0.000000000000 RREN_SUPPre-tx support 0.000000000000 RAGE 0.000000000000 WAITLIST_TIME 0.960000000000 TX_YR 0.000000000000 HCV1 0.930000000000 HCC1 0.000000000000 NASH1 0.150000000000 ALD1 0.001000000000 CIT 0.000000000047 DAGE 0.000000000000 DSEXMale 0.041000000000
Random effects Group Variable Std Dev Variance TRANSPLANT_UNIT Intercept 0.1764769 0.0311441
confint.coxme(cox2)
2.5 % 97.5 %
LDLT1 -0.08559574530 0.14777676912 BMI -0.00936653084 -0.00282369512 RBILIRUBIN 0.00112726004 0.00486937654 RINR -0.01453064178 0.01438946014 RCREAT 0.06438493781 0.09674977224 RREN_SUPPre-tx support 0.30043858591 0.43043420319 RAGE 0.01716438588 0.02133608706 WAITLIST_TIME -0.00003540303 0.00003699483 TX_YR -0.05397101726 -0.04027311779 HCV1 -0.08310431010 0.07593995147 HCC1 0.16358953883 0.25030877885 NASH1 -0.09668361129 0.01551133628 ALD1 -0.15457607182 -0.03755810648 CIT 0.00022944223 0.00042988633 DAGE 0.00656595551 0.00878138539 DSEXMale 0.00079610367 0.07371885125
#Uni
cox1 <- coxph(Surv(PSURV,PCENS)~ LDLT, data=CAN)
forest_model(cox1)
## Resized limits to included dashed line in forest panel
#Just recipient
cox1 <- coxme(formula= Surv(PSURV_years,PCENS)~LDLT+
# RSEX+
BMI+
RBILIRUBIN+
RINR+
RCREAT+
RREN_SUP+
RAGE+
WAITLIST_TIME+
TX_YR+
HCV+
HCC+
NASH+
ALD+
(1|TRANSPLANT_UNIT),
data=CAN)
cox1
Cox mixed-effects model fit by maximum likelihood Data: CAN events, n = 275, 1562 (2064 observations deleted due to missingness) Iterations= 8 42 NULL Integrated Fitted Log-likelihood -1911.173 -1894.755 -1894.754
Chisq df p AIC BIC
Integrated loglik 32.84 14 0.0030410 4.84 -45.80 Penalized loglik 32.84 13 0.0018026 6.84 -40.19
Model: Surv(PSURV_years, PCENS) ~ LDLT + BMI + RBILIRUBIN + RINR + RCREAT + RREN_SUP + RAGE + WAITLIST_TIME + TX_YR + HCV + HCC + NASH + ALD + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p LDLT1 -0.4059059463 0.6663728 0.2139677437 -1.90 0.0580 BMI 0.0070975297 1.0071228 0.0047597144 1.49 0.1400 RBILIRUBIN -0.0009452539 0.9990552 0.0058360665 -0.16 0.8700 RINR 0.0830430941 1.0865886 0.0662524387 1.25 0.2100 RCREAT 0.0900951266 1.0942784 0.0623984713 1.44 0.1500 RREN_SUPPre-tx support 0.0521201370 1.0535023 0.6229937905 0.08 0.9300 RAGE 0.0213351425 1.0215644 0.0070187279 3.04 0.0024 WAITLIST_TIME -0.0000195753 0.9999804 0.0001664136 -0.12 0.9100 TX_YR -0.0053806576 0.9946338 0.0240358638 -0.22 0.8200 HCV1 0.2394025201 1.2704898 0.1347490888 1.78 0.0760 HCC1 0.1125268684 1.1191023 0.1453354977 0.77 0.4400 NASH1 0.1353453565 1.1449321 0.2303036131 0.59 0.5600 ALD1 -0.0874127530 0.9162988 0.1560258829 -0.56 0.5800
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.00397686599 0.00001581546
confint.coxme(cox1)
2.5 % 97.5 %
LDLT1 -0.8338414337 0.0220295411 BMI -0.0024218990 0.0166169585 RBILIRUBIN -0.0126173868 0.0107268790 RINR -0.0494617832 0.2155479715 RCREAT -0.0347018161 0.2148920693 RREN_SUPPre-tx support -1.1938674439 1.2981077180 RAGE 0.0072976867 0.0353725982 WAITLIST_TIME -0.0003524025 0.0003132519 TX_YR -0.0534523852 0.0426910700 HCV1 -0.0300956576 0.5089006977 HCC1 -0.1781441270 0.4031978638 NASH1 -0.3252618697 0.5959525826 ALD1 -0.3994645188 0.2246390129
#Just donor
cox1 <- coxme(formula= Surv(PSURV_years,PCENS)~LDLT+
CIT+
DAGE+
DSEX+
TX_YR+
(1|TRANSPLANT_UNIT),
data=CAN)
cox1
Cox mixed-effects model fit by maximum likelihood Data: CAN events, n = 387, 2717 (909 observations deleted due to missingness) Iterations= 7 37 NULL Integrated Fitted Log-likelihood -2896.113 -2877.986 -2877.181
Chisq df p AIC BIC
Integrated loglik 36.25 6.00 0.00000246110 24.25 0.50 Penalized loglik 37.86 5.61 0.00000079486 26.63 4.41
Model: Surv(PSURV_years, PCENS) ~ LDLT + CIT + DAGE + DSEX + TX_YR + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p LDLT1 -0.5285835670 0.5894393 0.2274256290 -2.32 0.0200 CIT 0.0005932012 1.0005934 0.0002719127 2.18 0.0290 DAGE 0.0088863681 1.0089260 0.0030151317 2.95 0.0032 DSEXMale 0.0702133296 1.0727370 0.1138151879 0.62 0.5400 TX_YR -0.0192695022 0.9809150 0.0200001840 -0.96 0.3400
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.095737942 0.009165753
confint.coxme(cox1)
2.5 % 97.5 %
LDLT1 -0.98343482505 -0.073732309 CIT 0.00004937588 0.001137027 DAGE 0.00285610479 0.014916631 DSEXMale -0.15741704618 0.297843705 TX_YR -0.05926987022 0.020730866
#donor and recipient
cox2 <- coxme(formula= Surv(PSURV_years,PCENS)~LDLT+
# RSEX+
BMI+
RBILIRUBIN+
RINR+
RCREAT+
RREN_SUP+
RAGE+
WAITLIST_TIME+
TX_YR+
HCV+
HCC+
NASH+
ALD+
CIT+
DAGE+
DSEX+
(1|TRANSPLANT_UNIT),
data=CAN)
cox2
Cox mixed-effects model fit by maximum likelihood Data: CAN events, n = 209, 1174 (2452 observations deleted due to missingness) Iterations= 5 27 NULL Integrated Fitted Log-likelihood -1382.751 -1363.347 -1363.345
Chisq df p AIC BIC
Integrated loglik 38.81 17 0.0018995 4.81 -52.01 Penalized loglik 38.81 16 0.0011587 6.81 -46.68
Model: Surv(PSURV_years, PCENS) ~ LDLT + BMI + RBILIRUBIN + RINR + RCREAT + RREN_SUP + RAGE + WAITLIST_TIME + TX_YR + HCV + HCC + NASH + ALD + CIT + DAGE + DSEX + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p LDLT1 -0.42375444431 0.6545846 0.3838033773 -1.10 0.2700 BMI 0.00256551883 1.0025688 0.0075680658 0.34 0.7300 RBILIRUBIN 0.00517942937 1.0051929 0.0062152900 0.83 0.4000 RINR 0.06956679059 1.0720437 0.0746042913 0.93 0.3500 RCREAT 0.03016805276 1.0306277 0.0736548232 0.41 0.6800 RREN_SUPPre-tx support -0.69335897371 0.4998941 1.0321275595 -0.67 0.5000 RAGE 0.02465131144 1.0249577 0.0082866567 2.97 0.0029 WAITLIST_TIME 0.00006299711 1.0000630 0.0001813164 0.35 0.7300 TX_YR 0.01008658292 1.0101376 0.0277848762 0.36 0.7200 HCV1 0.25178213497 1.2863158 0.1547902429 1.63 0.1000 HCC1 0.05001748240 1.0512895 0.1659043526 0.30 0.7600 NASH1 0.20608716499 1.2288603 0.2522424712 0.82 0.4100 ALD1 -0.09222770075 0.9118975 0.1751831913 -0.53 0.6000 CIT 0.00067445016 1.0006747 0.0003391319 1.99 0.0470 DAGE 0.00697116294 1.0069955 0.0042288651 1.65 0.0990 DSEXMale 0.20116657697 1.2228284 0.1653912376 1.22 0.2200
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.00893572849 0.00007984724
confint.coxme(cox2)
2.5 % 97.5 %
LDLT1 -1.191361198938 0.3438523103 BMI -0.012570612815 0.0177016505 RBILIRUBIN -0.007251150718 0.0176100095 RINR -0.079641792050 0.2187753732 RCREAT -0.117141593736 0.1774776992 RREN_SUPPre-tx support -2.757614092748 1.3708961453 RAGE 0.008077998027 0.0412246248 WAITLIST_TIME -0.000299635668 0.0004256299 TX_YR -0.045483169544 0.0656563354 HCV1 -0.057798350760 0.5613626207 HCC1 -0.281791222839 0.3818261876 NASH1 -0.298397777346 0.7105721073 ALD1 -0.442594083386 0.2581386819 CIT -0.000003813729 0.0013527141 DAGE -0.001486567298 0.0154288932 DSEXMale -0.129615898176 0.5319490521
#Uni
cox1 <- coxph(Surv(PSURV,PCENS)~ LDLT, data=UK)
forest_model(cox1)
#Just recipient
cox1 <- coxme(formula= Surv(PSURV_years,PCENS)~LDLT+
#RSEX+
BMI+
RBILIRUBIN+
RINR+
RCREAT+
RREN_SUP+
RAGE+
WAITLIST_TIME+
TX_YR+
HCV+
HCC+
NASH+
ALD+
(1|TRANSPLANT_UNIT),
data=UK)
cox1
Cox mixed-effects model fit by maximum likelihood Data: UK events, n = 1052, 6140 (455 observations deleted due to missingness) Iterations= 12 63 NULL Integrated Fitted Log-likelihood -8606.923 -8525.364 -8517.52
Chisq df p AIC BIC
Integrated loglik 163.12 14.00 0 135.12 65.70 Penalized loglik 178.81 17.93 0 142.95 54.05
Model: Surv(PSURV_years, PCENS) ~ LDLT + BMI + RBILIRUBIN + RINR + RCREAT + RREN_SUP + RAGE + WAITLIST_TIME + TX_YR + HCV + HCC + NASH + ALD + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z LDLT1 0.12634361188 1.1346720 0.2928621115 0.43 BMI -0.00434497430 0.9956645 0.0065488845 -0.66 RBILIRUBIN 0.01021981629 1.0102722 0.0050377501 2.03 RINR 0.00008861724 1.0000886 0.0057725146 0.02 RCREAT 0.22575233159 1.2532652 0.0681898259 3.31 RREN_SUPPre-tx support 0.27897102663 1.3217690 0.2456649159 1.14 RAGE 0.02092464308 1.0211451 0.0033606007 6.23 WAITLIST_TIME 0.00042653744 1.0004266 0.0001707659 2.50 TX_YR -0.03293101665 0.9676053 0.0123964167 -2.66 HCV1 0.17183808566 1.1874855 0.0807892999 2.13 HCC1 0.41367085416 1.5123593 0.0735249789 5.63 NASH1 0.33846475309 1.4027923 0.1022330965 3.31 ALD1 -0.03022211292 0.9702300 0.0711517571 -0.42 p LDLT1 0.67000000000 BMI 0.51000000000 RBILIRUBIN 0.04200000000 RINR 0.99000000000 RCREAT 0.00093000000 RREN_SUPPre-tx support 0.26000000000 RAGE 0.00000000048 WAITLIST_TIME 0.01200000000 TX_YR 0.00790000000 HCV1 0.03300000000 HCC1 0.00000001800 NASH1 0.00093000000 ALD1 0.67000000000
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.19094314 0.03645928
confint.coxme(cox1)
2.5 % 97.5 %
LDLT1 -0.45938061112 0.7120678349 BMI -0.01744274335 0.0087527947 RBILIRUBIN 0.00014431618 0.0202953164 RINR -0.01145641189 0.0116336464 RCREAT 0.08937267981 0.3621319834 RREN_SUPPre-tx support -0.21235880514 0.7703008584 RAGE 0.01420344177 0.0276458444 WAITLIST_TIME 0.00008500563 0.0007680693 TX_YR -0.05772385011 -0.0081381832 HCV1 0.01025948576 0.3334166855 HCC1 0.26662089632 0.5607208120 NASH1 0.13399856012 0.5429309461 ALD1 -0.17252562711 0.1120814013
#Just donor
cox1 <- coxme(formula= Surv(PSURV_years,PCENS)~LDLT+
CIT+
DAGE+
DSEX+
TX_YR+
(1|TRANSPLANT_UNIT),
data=UK)
cox1
Cox mixed-effects model fit by maximum likelihood Data: UK events, n = 1136, 6529 (66 observations deleted due to missingness) Iterations= 5 23 NULL Integrated Fitted Log-likelihood -9342.578 -9320.271 -9312.962
Chisq df p AIC BIC
Integrated loglik 44.61 6.00 0.0000000558650 32.61 2.40 Penalized loglik 59.23 9.77 0.0000000040377 39.70 -9.47
Model: Surv(PSURV_years, PCENS) ~ LDLT + CIT + DAGE + DSEX + TX_YR + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p LDLT1 0.01895274899 1.0191335 0.3010405209 0.06 0.95000000 CIT -0.00008644641 0.9999136 0.0001927383 -0.45 0.65000000 DAGE 0.00965224088 1.0096990 0.0019291147 5.00 0.00000056 DSEXMale 0.15353163218 1.1659447 0.0603082673 2.55 0.01100000 TX_YR -0.02737715959 0.9729942 0.0114388235 -2.39 0.01700000
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.16737583 0.02801467
confint.coxme(cox1)
2.5 % 97.5 %
LDLT1 -0.583128293 0.6210337907 CIT -0.000471923 0.0002990302 DAGE 0.005794011 0.0135104703 DSEXMale 0.032915098 0.2741481668 TX_YR -0.050254807 -0.0044995126
#donor and recipient
cox2 <- coxme(formula= Surv(PSURV_years,PCENS)~LDLT+
#RSEX+
BMI+
RBILIRUBIN+
RINR+
RCREAT+
RREN_SUP+
RAGE+
WAITLIST_TIME+
TX_YR+
HCV+
HCC+
NASH+
ALD+
CIT+
DAGE+
DSEX+
(1|TRANSPLANT_UNIT),
data=UK)
cox2
Cox mixed-effects model fit by maximum likelihood Data: UK events, n = 1046, 6085 (510 observations deleted due to missingness) Iterations= 16 83 NULL Integrated Fitted Log-likelihood -8550.296 -8457.482 -8449.321
Chisq df p AIC BIC
Integrated loglik 185.63 17.00 0 151.63 67.43 Penalized loglik 201.95 21.01 0 159.94 55.90
Model: Surv(PSURV_years, PCENS) ~ LDLT + BMI + RBILIRUBIN + RINR + RCREAT + RREN_SUP + RAGE + WAITLIST_TIME + TX_YR + HCV + HCC + NASH + ALD + CIT + DAGE + DSEX + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p LDLT1 0.1862613637 1.2047371 0.3152597592 0.59 0.5500000000 BMI -0.0073422704 0.9926846 0.0066779223 -1.10 0.2700000000 RBILIRUBIN 0.0112300085 1.0112933 0.0050507896 2.22 0.0260000000 RINR 0.0029691851 1.0029736 0.0058647373 0.51 0.6100000000 RCREAT 0.2239583521 1.2510189 0.0683259823 3.28 0.0010000000 RREN_SUPPre-tx support 0.2913084441 1.3381773 0.2458794354 1.18 0.2400000000 RAGE 0.0200886458 1.0202918 0.0033910757 5.92 0.0000000031 WAITLIST_TIME 0.0004402304 1.0004403 0.0001705023 2.58 0.0098000000 TX_YR -0.0386085630 0.9621272 0.0125176971 -3.08 0.0020000000 HCV1 0.1797297516 1.1968939 0.0812424548 2.21 0.0270000000 HCC1 0.4106019312 1.5077251 0.0738168431 5.56 0.0000000270 NASH1 0.3269844847 1.3867800 0.1023755104 3.19 0.0014000000 ALD1 -0.0538695516 0.9475557 0.0714679768 -0.75 0.4500000000 CIT -0.0001846231 0.9998154 0.0002026372 -0.91 0.3600000000 DAGE 0.0090706698 1.0091119 0.0020469162 4.43 0.0000094000 DSEXMale 0.1316862255 1.1407503 0.0645943784 2.04 0.0410000000
Random effects Group Variable Std Dev Variance TRANSPLANT_UNIT Intercept 0.2022269 0.0408957
confint.coxme(cox2)
2.5 % 97.5 %
LDLT1 -0.44425815480 0.8167808821 BMI -0.02069811493 0.0060135741 RBILIRUBIN 0.00112842931 0.0213315878 RINR -0.00876028942 0.0146986596 RCREAT 0.08730638759 0.3606103166 RREN_SUPPre-tx support -0.20045042664 0.7830673148 RAGE 0.01330649447 0.0268707972 WAITLIST_TIME 0.00009922576 0.0007812350 TX_YR -0.06364395725 -0.0135731688 HCV1 0.01724484203 0.3422146611 HCC1 0.26296824495 0.5582356174 NASH1 0.12223346386 0.5317355055 ALD1 -0.19680550521 0.0890664021 CIT -0.00058989758 0.0002206513 DAGE 0.00497683747 0.0131645022 DSEXMale 0.00249746881 0.2608749823
#bound4 <- bound2 %>% filter(COUNTRY == "US" | COUNTRY == "UK") %>% droplevels()
cox2 <- coxme(formula= Surv(PSURV_years,PCENS)~COUNTRY+
BMI+
RBILIRUBIN+
RINR+
RCREAT+
RAGE+
WAITLIST_TIME+
TX_YR+
CIT+
DAGE+
DSEX+
(1|TRANSPLANT_UNIT),
data=HCConly)
cox2
Cox mixed-effects model fit by maximum likelihood Data: HCConly events, n = 83, 412 (119 observations deleted due to missingness) Iterations= 10 83 NULL Integrated Fitted Log-likelihood -439.6867 -429.4036 -423.9747
Chisq df p AIC BIC
Integrated loglik 20.57 13.00 0.081967 -5.43 -36.88 Penalized loglik 31.42 16.61 0.015243 -1.81 -41.99
Model: Surv(PSURV_years, PCENS) ~ COUNTRY + BMI + RBILIRUBIN + RINR + RCREAT + RAGE + WAITLIST_TIME + TX_YR + CIT + DAGE + DSEX + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p COUNTRYCAN -1.6247605184 0.1969588 1.0646975105 -1.53 0.1300 COUNTRYUK -0.9772316089 0.3763515 1.0315723031 -0.95 0.3400 BMI 0.0041799280 1.0041887 0.0246082606 0.17 0.8700 RBILIRUBIN 0.0240632878 1.0243551 0.0393129404 0.61 0.5400 RINR -0.2566967389 0.7736028 0.4029856492 -0.64 0.5200 RCREAT 0.4439281176 1.5588184 0.1519691009 2.92 0.0035 RAGE 0.0169760373 1.0171209 0.0149045320 1.14 0.2500 WAITLIST_TIME -0.0001169403 0.9998831 0.0002426874 -0.48 0.6300 TX_YR -0.0281049872 0.9722863 0.0477786972 -0.59 0.5600 CIT -0.0024846183 0.9975185 0.0019311366 -1.29 0.2000 DAGE 0.0231877121 1.0234586 0.0112523078 2.06 0.0390 DSEXMale 0.1126158829 1.1192019 0.2314081284 0.49 0.6300
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.29977763 0.08986663
confint.coxme(cox2)
2.5 % 97.5 %
COUNTRYCAN -3.7541555393 0.5046345025 COUNTRYUK -3.0403762150 1.0859129973 BMI -0.0450365933 0.0533964493 RBILIRUBIN -0.0545625929 0.1026891685 RINR -1.0626680373 0.5492745595 RCREAT 0.1399899158 0.7478663194 RAGE -0.0128330267 0.0467851013 WAITLIST_TIME -0.0006023152 0.0003684345 TX_YR -0.1236623817 0.0674524072 CIT -0.0063468915 0.0013776549 DAGE 0.0006830964 0.0456923277 DSEXMale -0.3502003739 0.5754321398
#nonhCC
cox2 <- coxme(formula= Surv(PSURV_years,PCENS)~COUNTRY+
BMI+
RBILIRUBIN+
RINR+
RCREAT+
RAGE+
WAITLIST_TIME+
TX_YR+
CIT+
DAGE+
DSEX+
(1|TRANSPLANT_UNIT),
data=nonHCC)
cox2
Cox mixed-effects model fit by maximum likelihood Data: nonHCC events, n = 288, 2047 (508 observations deleted due to missingness) Iterations= 12 75 NULL Integrated Fitted Log-likelihood -2037.792 -2009.221 -2003.391
Chisq df p AIC BIC
Integrated loglik 57.14 13.00 0.00000016946 31.14 -16.48 Penalized loglik 68.80 17.18 0.00000003947 34.45 -28.46
Model: Surv(PSURV_years, PCENS) ~ COUNTRY + BMI + RBILIRUBIN + RINR + RCREAT + RAGE + WAITLIST_TIME + TX_YR + CIT + DAGE + DSEX + (1 | TRANSPLANT_UNIT) Fixed coefficients coef exp(coef) se(coef) z p COUNTRYCAN -0.66510133495 0.5142214 0.4160242667 -1.60 0.110000 COUNTRYUK 0.30639549620 1.3585195 0.3381680739 0.91 0.360000 BMI -0.04231457737 0.9585682 0.0129733924 -3.26 0.001100 RBILIRUBIN -0.00648612545 0.9935349 0.0137029598 -0.47 0.640000 RINR -0.00816335053 0.9918699 0.0949537812 -0.09 0.930000 RCREAT 0.30125511582 1.3515541 0.0897524503 3.36 0.000790 RAGE 0.02187913711 1.0221202 0.0052581897 4.16 0.000032 WAITLIST_TIME -0.00002206554 0.9999779 0.0001067695 -0.21 0.840000 TX_YR -0.02860797099 0.9717974 0.0221015889 -1.29 0.200000 CIT 0.00016631280 1.0001663 0.0002033148 0.82 0.410000 DAGE 0.01624278227 1.0163754 0.0058167755 2.79 0.005200 DSEXMale 0.06078621735 1.0626717 0.1201231436 0.51 0.610000
Random effects Group Variable Std Dev Variance
TRANSPLANT_UNIT Intercept 0.16078798 0.02585278
confint.coxme(cox2)
2.5 % 97.5 %
COUNTRYCAN -1.4971498684 0.1669471985 COUNTRYUK -0.3699406516 0.9827316440 BMI -0.0682613621 -0.0163677926 RBILIRUBIN -0.0338920451 0.0209197942 RINR -0.1980709130 0.1817442119 RCREAT 0.1217502152 0.4807600164 RAGE 0.0113627577 0.0323955165 WAITLIST_TIME -0.0002356044 0.0001914734 TX_YR -0.0728111489 0.0155952069 CIT -0.0002403168 0.0005729424 DAGE 0.0046092313 0.0278763332 DSEXMale -0.1794600699 0.3010325045
#Landmark at 90 days
cox1 <- coxph(formula= Surv(PSURV_years,PCENS)~bound4$COUNTRY+
bound4$CIT+
bound4$RSEX+
bound4$BMI+
bound4$RBILIRUBIN+
bound4$RINR+
bound4$RCREAT+
bound4$RREN_SUP+
bound4$DAGE+
bound4$RAGE+
bound4$WAITLIST_TIME+
bound4$DSEX+
bound4$TX_YR+
bound4$HCC+
bound4$PSC+
bound4$ALD+
bound4$HCV+
bound4$Centerpercentile,
subset = PSURV_years <= 0.2464066,
data=bound4)
forest_model(cox1)
## Warning in recalculate_width_panels(panel_positions, mapped_text =
## mapped_text, : Unable to resize forest panel to be smaller than its heading;
## consider a smaller text size
#Landmark 90 days to 2 years
cox1 <- coxph(formula= Surv(PSURV_years,PCENS)~bound4$COUNTRY+
bound4$CIT+
bound4$RSEX+
bound4$BMI+
bound4$RBILIRUBIN+
bound4$RINR+
bound4$RCREAT+
bound4$RREN_SUP+
bound4$DAGE+
bound4$RAGE+
bound4$WAITLIST_TIME+
bound4$DSEX+
bound4$TX_YR+
bound4$HCC+
bound4$PSC+
bound4$ALD+
bound4$HCV+
bound4$Centerpercentile,
subset = PSURV_years > 0.2464066 & PSURV_years <= 2,
data=bound4)
forest_model(cox1)
## Warning in recalculate_width_panels(panel_positions, mapped_text =
## mapped_text, : Unable to resize forest panel to be smaller than its heading;
## consider a smaller text size
#Landmark 5
cox1 <- coxph(formula= Surv(PSURV_years,PCENS)~bound4$COUNTRY+
bound4$CIT+
bound4$RSEX+
bound4$BMI+
bound4$RBILIRUBIN+
bound4$RINR+
bound4$RCREAT+
bound4$RREN_SUP+
bound4$DAGE+
bound4$RAGE+
bound4$WAITLIST_TIME+
bound4$DSEX+
bound4$TX_YR+
bound4$HCC+
bound4$PSC+
bound4$ALD+
bound4$HCV+
bound4$Centerpercentile,
subset = PSURV_years > 2 & PSURV_years <= 5,
data=bound4)
## Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
## Loglik converged before variable 9 ; coefficient may be infinite.
forest_model(cox1)
## Warning in recalculate_width_panels(panel_positions, mapped_text =
## mapped_text, : Unable to resize forest panel to be smaller than its heading;
## consider a smaller text size
library(scales)
#SEnd Gabi RDS file.
Trendsovertime <- bound4 %>% select(TX_YR, COUNTRY)
Trendsovertime <- Trendsovertime %>% group_by(TX_YR, COUNTRY) %>% mutate(count = n())
Trendsovertime <- Trendsovertime %>% group_by(TX_YR) %>% mutate(countyr = n())
Trendsovertime <- Trendsovertime %>% mutate(percentage = count/countyr)
#Counts per year
ggplot(Trendsovertime, aes(factor(TX_YR), y = count, group = COUNTRY,
color = COUNTRY)) +
geom_line(size = 1.5, alpha = 0.8) +
geom_point(size = 2) +
scale_color_npg(name="Country") +
#scale_color_brewer(name = "Etiology", palette = "Set1")+
theme_test(base_size = 32) +
xlab("Year of transplant") +
ylab("Number of transplants") +
scale_x_discrete(expand = expansion(mult = c(0, 0))) +
scale_y_continuous(expand = expansion(mult = c(0, 0)), limits = c(0,350)) +
annotate("text", x = 8, y = 70, label = "Canada: Cox-Stuart trend test p=0.49", size = 5) +
annotate("text", x = 8, y = 20, label = "UK: Cox-Stuart trend test p=0.73", size = 5) +
annotate("text", x = 6, y = 280, label = "US: Cox-Stuart trend test p=0.08", size = 5)
COUNTYEAR <- bound3 %>% group_by(TX_YR, COUNTRY) %>% mutate(countYEAR = n()) %>% ungroup()
COUNTYEAR <- COUNTYEAR %>% select(TX_YR, COUNTRY, countYEAR)
LDLT <- bound5 %>% filter(LDLT=="1" & GRAFT_TYPE == "Segmental") %>% group_by(TX_YR, COUNTRY) %>% mutate(countLDLT = n()) %>% ungroup()
LDLT <- LDLT %>% select(TX_YR, COUNTRY, countLDLT)
LDLT <- distinct(TX_YR, COUNTRY)
unique_rows1 <- !duplicated(LDLT[c("TX_YR","COUNTRY")])
unique.df1 <- LDLT[unique_rows1,]
unique_rows2 <- !duplicated(COUNTYEAR[c("TX_YR","COUNTRY")])
unique.df2 <- COUNTYEAR[unique_rows2,]
unique.df2
unique.df1
test <- merge(unique.df2, unique.df1, by=c("TX_YR", "COUNTRY"))
test
ggplot(test, aes(factor(TX_YR), y = countLDLT, group = COUNTRY,
color = COUNTRY)) +
geom_area(size = 1.5, alpha = 0.8, color_palette()) +
geom_area(data=test, aes(factor(TX_YR), y=countYEAR, group = COUNTRY, color=COUNTRY), size = 1.5, alpha=0.1) +
scale_color_jama(name="Country") +
theme_test(base_size = 18) +
xlab("Year of transplant") +
ylab("Number of transplants") +
facet_grid(.~COUNTRY, scales="free_y")+
scale_x_discrete(expand = expansion(mult = c(0, 0)), breaks=seq(2008, 2018, 4)) +
scale_y_continuous(expand = expansion(mult = c(0, 0)), limits = c(0, 6660))
#saveRDS(bound3, file = "/Users/Ivanics/Desktop/Research/104. UK vs. US. vs. CAN LDLT/Analysis/bound3forgraph.rds")
#Cox-stuart trend test Canada
library(trend)
Canada <- Trendsovertime %>% filter(COUNTRY == "CAN") %>% select(count, TX_YR)
x <- Canada %>% distinct(TX_YR, .keep_all = T) %>% arrange((TX_YR)) %>% as.data.frame()
x
x <- x[, "count"]
bartels.test(x)
cs.test(x)
#Canada proportion
x <- c(0.201, 0.172, 0.164, 0.150, 0.185, 0.164, 0.171, 0.156, 0.113, 0.110, 0.138)
cs.test(x)
#Cox-stuart trend test UK
UK <- Trendsovertime %>% filter(COUNTRY == "UK") %>% select(count, TX_YR)
x <- UK %>% distinct(TX_YR, .keep_all = T) %>% arrange((TX_YR)) %>% as.data.frame()
x
x <- x[, "count"]
bartels.test(x)
cs.test(x)
x <- c(0.0169, 0.0218, 0.0123, 0.0191, 0.0178, 0.0178, 0.0119, 0.024, 0.0148, 0.00824, 0.00389)
cs.test(x)
#Cox-stuart trend test US
US <- Trendsovertime %>% filter(COUNTRY == "US") %>% select(count, TX_YR)
x <- US %>% distinct(TX_YR, .keep_all = T) %>% arrange((TX_YR)) %>% as.data.frame()
x
x <- x[, "count"]
bartels.test(x)
cs.test(x)
x <- c(0.0341, 0.0324, 0.0419, 0.0366, 0.0381, 0.0406, 0.0418, 0.0491, 0.0451,0.0456, 0.05)
cs.test(x)
Trendsovertimedonortype <- bound3 %>% select(TX_YR, DTYPE, COUNTRY)
Trendsovertimedonortype <- Trendsovertimedonortype %>% group_by(TX_YR, COUNTRY, DTYPE) %>% mutate(count = n()) %>% ungroup()
Trendsovertimedonortype <- Trendsovertimedonortype %>% group_by(TX_YR, COUNTRY) %>% mutate(countyr = n()) %>% ungroup()
Trendsovertimedonortype <- Trendsovertimedonortype %>% mutate(percentage = count/countyr)
Trendsovertimedonortype <- Trendsovertimedonortype %>% filter(DTYPE == "LDLT")
Trendsovertimedonortype %>% group_by(COUNTRY, TX_YR, percentage) %>% count() %>% ungroup() %>% print(n=40)
Percentage <- c(0.0341, 0.0324, 0.0419, 0.0366, 0.0381, 0.0406, 0.0418, 0.0491, 0.0451,0.0456, 0.05, 0.201, 0.172, 0.164, 0.150, 0.185, 0.164, 0.171, 0.156, 0.113, 0.110, 0.138, 0.0169, 0.0218, 0.0123, 0.0191, 0.0178, 0.0178, 0.0119, 0.024, 0.0148, 0.00824, 0.00389)
Country <- c("US", "US", "US", "US", "US", "US", "US", "US", "US", "US", "US", "CAN", "CAN", "CAN", "CAN", "CAN" , "CAN", "CAN", "CAN", "CAN", "CAN", "CAN", "UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK")
Year <- c(2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018)
Trendsovertimedonortype <- data_frame(Percentage, Country, Year)
Trendsovertimedonortype$Country <- factor(Trendsovertimedonortype$Country)
Trendsovertimedonortype$Country <- relevel(Trendsovertimedonortype$Country, "US")
#Counts per year
ggplot(Trendsovertimedonortype, aes(factor(Year), y = Percentage*100, group = Country,
color = Country)) +
geom_line(size = 1.5, alpha = 0.8) +
geom_point(size = 2) +
scale_color_npg(name="Country") +
#scale_color_brewer(name = "Etiology", palette = "Set1")+
theme_test(base_size = 32) +
xlab("Year of transplant") +
ylab("Percent of transplants (%)") +
scale_x_discrete(expand = expansion(mult = c(0, 0))) +
scale_y_continuous(expand = expansion(mult = c(0, 0)), limits = c(0,25)) +
annotate("text", x = 8, y = 19, label = "Canada: Cox-Stuart trend test p=0.08", size = 5) +
annotate("text", x = 8, y = 3, label = "UK: Cox-Stuart trend test p=0.49", size = 5) +
annotate("text", x = 8, y = 6, label = "US: Cox-Stuart trend test p=0.08", size = 5)
Multiple imputations
library(mice)
UKvUSvCANvsUHNforimputation <- bound2 %>% select(TX_YR, RAGE, PSURV, PCENS, DAGE, DTYPE, DBMI, DCMV, DSEX, BLD_GP_MATCH, GRAFT_TYPE, CIT, RSEX, RETHNIC, BMI, WAITLIST_TIME, TRANSPLANT_UNIT, MELD, RREN_SUP, RVENT, RAB_SURGERY, RLIFE, RASCITES, RENCEPH, RBG, RANTI_HCV, RALBUMIN, RINR, RBILIRUBIN, RCREAT, COUNTRY, UKT_PLDGRP, NASH, PSURV_years, PSURV_1year, PCENS_1year, PSURV_3year, PCENS_3year, PSURV_5year, PCENS_5year, UHN)
sapply(UKvUSvCANvsUHNforimputation, function(x) sum(is.na(x)))
#Imputation
init1 <- mice(UKvUSvCANvsUHNforimputation, m=20, maxit=20, seed = 999)
init1$method
imputed <- complete(init1, 3)
summary(init1)
#densityplot(init1)
sapply(UKvUSvCANvsUHNforimputation, function(x) sum(is.na(x)))
modelFitpre <- with(UKvUSvCANvsUHNforimputation,
coxph(formula= Surv(PSURV,PCENS)~COUNTRY+
CIT+
RSEX+
BMI+
RINR+
RBILIRUBIN+
RCREAT+
RREN_SUP+
DAGE+
RAGE+
WAITLIST_TIME+
DSEX+
GRAFT_TYPE+
TX_YR, data=UKvUSvCANvsUHNforimputation))
summary(modelFitpre)
#Uni
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY))
summary(pool(coximpute), conf.int = T, exponentiate = T)
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY, subset = PSURV_years <= 0.2464066))
summary(pool(coximpute), conf.int = T, exponentiate = T)
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY, subset = PSURV_years > 0.2464066 & PSURV_years <= 2))
summary(pool(coximpute), conf.int = T, exponentiate = T)
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY, subset = PSURV_years > 2 & PSURV_years <= 5))
summary(pool(coximpute), conf.int = T, exponentiate = T)
#Just recipient
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY+
DAGE+
DSEX+
CIT+
RSEX+
BMI+
RINR+
RBILIRUBIN+
RCREAT+
RREN_SUP+
RAGE+
WAITLIST_TIME+
TX_YR))
summary(pool(coximpute), conf.int = T, exponentiate = T)
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY+
DAGE+
DSEX+
CIT+
RSEX+
BMI+
RINR+
RBILIRUBIN+
RCREAT+
RREN_SUP+
RAGE+
WAITLIST_TIME+
TX_YR,
subset = PSURV_years <= 0.2464066))
summary(pool(coximpute), conf.int = T, exponentiate = T)
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY+
DAGE+
DSEX+
CIT +
RSEX+
BMI+
RINR+
RBILIRUBIN+
RCREAT+
RREN_SUP+
RAGE+
WAITLIST_TIME+
TX_YR,
subset = PSURV_years > 0.2464066 & PSURV_years <= 2))
summary(pool(coximpute), conf.int = T, exponentiate = T)
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY+
DAGE+
DSEX+
CIT+
RSEX+
BMI+
RINR+
RBILIRUBIN+
RCREAT+
RREN_SUP+
RAGE+
WAITLIST_TIME+
TX_YR,
subset = PSURV_years > 2 & PSURV_years <= 5))
summary(pool(coximpute), conf.int = T, exponentiate = T)
#Just donor
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY+
CIT+
DAGE+
DSEX+
TX_YR))
summary(pool(coximpute), conf.int = T, exponentiate = T)
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY+
CIT+
DAGE+
DSEX+
TX_YR,
subset = PSURV_years <= 0.2464066))
summary(pool(coximpute), conf.int = T, exponentiate = T)
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY+
CIT+
DAGE+
DSEX+
TX_YR,
subset = PSURV_years > 0.2464066 & PSURV_years <= 2))
summary(pool(coximpute), conf.int = T, exponentiate = T)
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY+
CIT+
DAGE+
DSEX+
TX_YR,
subset = PSURV_years > 2 & PSURV_years <= 5))
summary(pool(coximpute), conf.int = T, exponentiate = T)
#Overall
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY+
CIT+
RSEX+
BMI+
RINR+
RBILIRUBIN+
RCREAT+
RREN_SUP+
DAGE+
RAGE+
WAITLIST_TIME+
DSEX+
TX_YR))
summary(pool(coximpute), conf.int = T, exponentiate = T)
#Landmark 0-90 days
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY+
CIT+
RSEX+
BMI+
RINR+
RBILIRUBIN+
RCREAT+
RREN_SUP+
DAGE+
RAGE+
WAITLIST_TIME+
DSEX+
TX_YR,
subset = PSURV_years <= 0.2464066))
summary(pool(coximpute), conf.int = T, exponentiate = T)
#Landmark 90-days to 2 years
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY+
CIT+
RSEX+
BMI+
RINR+
RBILIRUBIN+
RCREAT+
RREN_SUP+
DAGE+
RAGE+
WAITLIST_TIME+
DSEX+
TX_YR,
subset = PSURV_years > 0.2464066 & PSURV_years <= 2))
summary(pool(coximpute), conf.int = T, exponentiate = T)
#Landmark 2years to 5 years
coximpute <- with(init1, coxph(Surv(PSURV,PCENS)~ COUNTRY+
CIT+
RSEX+
BMI+
RINR+
RBILIRUBIN+
RCREAT+
RREN_SUP+
DAGE+
RAGE+
WAITLIST_TIME+
DSEX+
TX_YR,
subset = PSURV_years > 2 & PSURV_years <= 5))
summary(pool(coximpute), conf.int = T, exponentiate = T)